System and method for monitoring parking enforcement officer performance in real time with the aid of a digital computer

ABSTRACT

A system and method for monitoring parking enforcement officer performance with the aid of a digital computer is provided. A time-based active representational model of the city is created by fusing sensory data collected from various sources around a city with numerical data gleaned from historical and on-going activities, including parking regulation citation and warning numbers, resource allocations, and so on. The model can be used to form quantitative predictions of expected violations, revenue stream, and so forth, that can then be used as recommendations as to where to enforce and when, so as to maximize the utilization of the limited resources represented by the officers on the street. Moreover, the performance of the officers can be weighed against expectations of performance postulated from the quantitative predictions.

FIELD

This application relates in general to parking enforcement, and inparticular to a system and method for monitoring parking enforcementofficer performance with the aid of a digital computer.

BACKGROUND

Parking enforcement organizations for a municipality, whether a city,town or other governmental subunit (henceforth, simply “city”), aretypically charged with managing the city's parking resources, directingtraffic, and promoting the public welfare, although some cities retainprivate contractors to handle parking enforcement or may reserve trafficdirection duties to police officers. Parking enforcement organizationsgenerate revenue from the issuing of citations (ticketing of non-movingviolations) for non-compliance with parking regulations, includingregulations governing where and when parking on the streets or othercity-regulated zones or spaces is permitted, restricted, prohibited, orcontrolled.

Although revenue from parking fines serves as a societal recompense forthe harm that illegal parking causes, parking enforcement also helpsfurther three public welfare objectives. The first objective ispromoting public safety and health, including providing access forpersons with disabilities. Obstructing a fire hydrant, blocking analley, parking in a fire lane and similar parking infractions put livesand property at risk, while illegally parking in a space reserved forpersons with disabilities wrongfully deprives the disabled of parking.The second objective is promoting the free flow of traffic and theregularity of parking turnover. Meter violations and overstaying aposted time limit wastes motorists' time and fuel and increasespollution if motorists are forced to search for other parking. The thirdobjective is improving livability. Cities strive to promote the qualityof life for its citizenry and visitors and some parking regulations areintended to punish motorists who park in ways that are a nuisance ordetract from a city's beauty or aesthetic.

Parking enforcement organizations are often formed as a part of thepolice force, or may be organized separately along similar lines, withan administrative hierarchy that includes, from the top down, managers,supervisors, dispatchers, and parking enforcement officers (PEOs).Managers are responsible for organizational performance, policy, andlong-term trends. Supervisors oversee the performance and assignments ofsquads or groups of officers. Dispatchers orchestrate responses tounplanned events reported through emergency services or othercommunications channels. Last, while on beats, officers performrevenue-producing enforcement activities and handle planned events, plusperform public safety-promoting service activities and handle unplannedevents assigned by dispatch.

The day-to-day operation of a parking enforcement organization requiresmaking countless operational decisions in a prompt manner based uponlimited data. Largely, parking enforcement officers work in virtualisolation from their peers; real time activities information is notshared between parking enforcement officers. This information vacuum canbe problematic. Often, parking enforcement officers returning fromresponding to unplanned events will issue citations to parking violatorsencountered while returning to their own beat, while the parkingenforcement officer on whose beat the parking violators were ticketedcould end up unnecessarily patrolling those areas, thereby wasting timeand energy. Similarly, dispatchers need to know the status of parkingenforcement officers at all times, so as to be able to assignappropriate resources to an event response, yet generally lack suchknowledge. Gaining situation awareness would require contacting eachparking enforcement officer whenever an unplanned event arose, yet thatapproach would impose constant dispatcher interruptions.

The lack of knowledge includes gaps in or unavailable sensor data, anabsence of predictive models, and insufficient analyses of historicalperformance. Thus, optimal recommendations for next activities remaininfeasible. Notwithstanding, urgent situations require fast actions andsometimes overriding organizational policy.

Therefore, a need remains for a providing the personnel working in aparking enforcement organization with the tools and informationnecessary to optimize performance in both compliance- andservice-related activities.

SUMMARY

A parking enforcement organization includes a hierarchy of personnelthat include parking enforcement officers working on the streets at thelowest tier, supervisors who manage the officers and dispatchers whohandle unplanned events and emergencies at the next tier, and managersresponsible for policy and overall performance at the top. Theseindividuals must work collaboratively, yet the work of parkingenforcement is inherently two-fold. Revenue-producing enforcementactivities and public safety-promoting service activities are at oddsbecause they place conflicting time demands on the same people.

A time-based active representational model of the city is created byfusing sensory data collected from various sources around a city withnumerical data gleaned from historical and on-going activities,including parking regulation citation and warning numbers, resourceallocations, and so on. The model can be used to form quantitativepredictions of expected violations, revenue stream, and so forth, thatcan then be used as recommendations as to where to enforce and when, soas to maximize the utilization of the limited resources represented bythe officers on the street. Moreover, information evaluated in the modelcan be the basis for finer-grained recommendations and alerts toofficers than a supervisor reasonably could. In addition, since officershave an immediate awareness of street conditions and other factors notvisible to supervisors, officers are provided with a level of detailthat is useful, but not overwhelming at an abstract or detailed level.

One embodiment provides a system and method for monitoring parkingenforcement officer performance in real time with the aid of a digitalcomputer. A beat within a city is defined for a parking enforcementofficer within which enforcement activities are to be performed by theofficer. Parking citation data is fused with information received fromsensors in the city into a time-based active representational model ofthe city that includes estimates of parking violations expected to occurwithin the beat. One or more activity plans are built for the officerbased upon the fused information from the active representational model.Those activity plans that optimize performance by the officer areidentified. Activities of the officer while on the beat are regularlytracked. Analytics based upon differences between the officer's expectedperformance according to the optimal activity plans versus the officer'sactual performance according to the officer's tracked activities arecreated.

The foregoing system and method address obstacles in optimizingorganizational performance by:

-   -   Providing data and recommendations in real time to officers,        supervisors, and dispatchers to guide their decision-making.    -   Combining real time situation and operational data from sensors        and human observers with data from databases about past        activities.    -   Making recommendations to guide enforcement decisions and        optimize performance by generating options, predicting and        comparing outcomes, and recommending optimal responses.    -   Presenting combined space/time/work information for activities        and operational choices in meaningful visual representations.    -   Combining human and computer cognition to take advantage of        differences in human and computer capabilities and blind spots.    -   Providing computer partners (c-partners) to efficiently        coordinate activities at all levels. The c-partners across the        organization connect to system elements for monitoring,        alerting, planning and recommending, and intermediate        communications between people.    -   Using computer intermediation and conditional autonomous        messaging to reduce the overhead of the communication and        negotiation needed for team coordination.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an organizational chart showing, by way of example, themanagerial hierarchy of a parking enforcement organization.

FIG. 2 is a block diagram showing a system for monitoring parkingenforcement officer performance with the aid of a digital computer inaccordance with one embodiment.

FIG. 3 is a functional block diagram showing a method for monitoringparking enforcement officer performance with the aid of a digitalcomputer in accordance with one embodiment.

FIG. 4 is a block diagram showing the architectural elements of theparking enforcement support services as implemented on the parkingenforcement support services server of FIG. 2.

FIGS. 5-7 are map diagrams respectively showing, by way of examples, anofficer activity interface presenting optimal patrol routes within abeat for different times of the day.

FIGS. 8-10 are diagrams respectively showing, by way of examples, anofficer activity interface presenting recommendations for probableparking violations along a patrol route as updated based upon parkingenforcement officer movement.

FIGS. 11-13 are diagrams respectively showing, by way of examples, anofficer activity interface presenting adaptive recommendations forprobable parking violations along a patrol route as updated when arecommendation is not followed.

FIG. 14 is a map diagram showing, by way of example, clusters ofpredicted or issued parking violations or citations.

FIG. 15 is a map diagram showing, by way of example, an activity map fora pair of parking enforcement officers while working on their respectivepatrol routes.

FIG. 16 is map diagram showing, by way of example, an activity type mapfor streets recently visited by a parking enforcement officer.

FIG. 17 is diagram showing, by way of example, a mapping of city blockarea name to city block area character for Beat 3 in Ocean City.

FIG. 18 is diagram showing, by way of example, visual encodings for theparking enforcement tasks.

FIGS. 19-22 are pairs of diagrams showing, by way of examples,prioritized parking enforcement tasks for the time periods of Scenario1.

FIG. 23 is a set of diagrams showing, by way of examples, an overview ofthe enforcement activities for Beat 3.

FIG. 24 is a diagram showing, by way of example, a logon screen of thereal time coordination application, which would be used by an officer atthe start of each duty shift.

FIG. 25 is a diagram showing, by way of example, the home screen of thereal time coordination application of FIG. 24.

FIG. 26 is a diagram showing, by way of example, a messaging interfacescreen of the real time coordination application of FIG. 24.

FIG. 27 is a diagram showing, by way of example, an activity mapinterface screen of the real time coordination application of FIG. 24.

FIG. 28 is a diagram showing, by way of example, an alternate activitymap interface screen of the real time coordination application of FIG.24.

FIG. 29 is a diagram showing, by way of example, an alternative activitymap interface screen of the real time coordination application of FIG.24.

FIG. 30 is a diagram showing, by way of example, a notetaking interfacescreen of the real time coordination application of FIG. 24.

FIG. 31 is a diagram showing, by way of example, an orienting interfacescreen of the real time coordination application of FIG. 24 showing arecommended route for parking enforcement.

FIG. 32 is a diagram showing, by way of example, an orienting interfacescreen of the real time coordination application of FIG. 24 focusing ona business area of the beat.

FIG. 33 is a diagram showing, by way of example, an orienting interfacescreen of the real time coordination application of FIG. 24 showingoptions for audio global positioning system (GPS) alerts.

FIG. 34 is a diagram showing, by way of example, a patrolling interfacescreen of the real time coordination application of FIG. 24.

FIG. 35 is a diagram showing, by way of example, an orienting interfacescreen of the real time coordination application of FIG. 24 showing arecommended route for parking enforcement in a portion of the beat.

FIG. 36 is a diagram showing, by way of example, an activity mapinterface screen of the real time coordination application of FIG. 24showing a portion of the beat.

FIG. 37 is a diagram showing, by way of example, a shift reviewinterface screen of the real time coordination application of FIG. 24.

FIG. 38 is a diagram showing, by way of example, a patrolling interfacescreen of the real time coordination application of FIG. 24.

FIG. 39 is a set of diagrams showing, by way of examples, changes induty shift task assignments for Adams, Baker and Cooper.

FIGS. 40-41 are sets of diagrams showing, by way of examples, plannedteam assignments respectively before and after an accident in a regionof the beat.

FIG. 42 is a diagram showing, by way of example, a patrolling interfacescreen of the real time coordination application of FIG. 24 showing there-assignment of duty shift tasks to cover the accident.

FIG. 43 is a set of diagrams showing, by way of examples, planned teamassignments for an afternoon.

FIG. 44 is a diagram showing, by way of example, a messaging interfacescreen of the real time coordination application of FIG. 24.

FIG. 45 is a diagram showing, by way of example, a patrolling interfacescreen of the real time coordination application of FIG. 24.

FIG. 46 is a diagram showing, by way of example, a situation assessmentinterface of a real time monitoring application for use by a parkingenforcement officer supervisor for execution on a personal computer.

FIG. 47 is a diagram showing, by way of example, a situation assessmentinterface display of the real time monitoring application of FIG. 46.

FIGS. 48-49 are diagrams showing, by way of examples, the situationassessment interface of the real time monitoring application of FIG. 46.

FIG. 50 is a pair of diagrams showing, by way of example, re-assignmentsof duty shift tasks.

DETAILED DESCRIPTION

Glossary

Organizational resources—the resources used by an organization chargedwith enforcing parking regulations through enforcement activities andpromoting public safety through service activities. Such resources caninclude funding, materials, staff, vehicles, and other assets that canbe drawn on by the parking enforcement organization to functioneffectively. For simplicity, an organization charged with enforcingparking regulations and promoting public safety parking will henceforthbe called a “parking enforcement organization” or just “organization,”regardless of whether the organization provides parking regulationenforcement, traffic direction, public safety promotion, or somecombination of the foregoing duty activities. Similarly, officerscharged with performing duties on behalf of an organization will becalled a “parking enforcement officer” or just “officer,” regardless ofwhether the officer duties that include enforcing parking regulations,directing traffic, promoting public safety, or some combination of theforegoing duty activities.

Organizational performance—performance analytics that measure how onparking enforcement organization fulfills its mission and goals asreflected by the activities performed and resources consumed.

Squads and teams—a squad refers to a group of officers reporting to asingle supervisor; a team refers to smaller groups of officers whocoordinate the performance of their respective duty activities during aduty shift.

Performance analytics—quantitative and qualitative measures that conveylevels and quality of parking enforcement organizational performance.

Performance tracking and evaluation—collecting and reporting data of howa parking enforcement organization performs duty activities over time.Performance tracking and evaluation involves comparing metrics of actualperformance to predictions or expectations of performance.

Events—happenings in a city bounded in time (when they occur) and space(where they occur), such as a fire downtown, a winter storm is an event,or a holiday parade.

Violation and citation—a violation is an action that violates(non-complies with) city parking regulations, ordinances, or directives(henceforth, simply “regulations”), while a citation is a notice(ticket) of a violation issued by a parking enforcement officer thattypically identifies the city parking regulation violated, documents thetime and place of the violation, indicates the fine levied, and providesa notice to appear in court if the violator wants to contest theviolation.

Compliance—refers to the degree to which people, that is, drivers,tourists, and so forth, adhere to the city regulations.

Violation lifecycle—the process whereby a violation arises, persists,and ends. For example, a violation for parking in a two-hour residentialzone occurs once a vehicle is parked for more than two hours. Thevehicle may be cited if a parking enforcement officer observes theviolation. The violation ends when the vehicle is either moved away orthe driver pays for more time, if allowed. Other violations, such asparking in a prohibited zone, begin as soon as a vehicle isimpermissibly parked.

Modeling—refers to creating computational models of how systems work.Models provide information about the state of a system by keeping trackof the state in terms of parameters and characterizing how outputparameters change in response to other parameters. Real time models areupdated according to real time data feeds about a system. Predictivemodels predict present or future state. For example, traffic speedvaries according to the amount of traffic and the capacity of the roads.Herein, models of enforcement organizational processes, models of citysituations and activities, and models of violations are discussed.

City modeling—refers to creating computational models for aspects of amunicipality. Through time-based active representational modeling, citymodels may be updated by real time information about city events andactivities, while specific predictive models of violations make use ofinformation from sensors, including locational data from GPS receiversin officers' vehicles or mobile computing devices, traffic loop sensors,parking meter payment data, car marking information from ALPR-equippedvehicles, and traffic flow sensor information, and probabilisticpredictions based upon historical and current data. For example, a citytraffic model could predict the amount of traffic in different parts ofa city based upon sensor and historical and current data. A parkingviolations model could predict the probabilities or likelihoods ofviolations in parts of a city based upon historical citation data,including spatially sampled or temporally sampled historical citationdata, additional factors, including categories or characteristics ofneighborhoods, data from parking enforcement officers in the field asthey issue citations, violation life-cycle models, or through predictivealgorithms.

Organizational modeling—refers to models of how organizations canrespond and act in a situation, given available resources, knownconstraints, and organizational priorities.

Policy parameters and priorities—refer to variables that representdifferent aspects and goals of enforcement policy. For example, a citymay want to prioritize enforcement of safety-related violations, balanceenforcement resources to provide different levels of coverage fordifferent neighborhoods, or guarantee response times to emergency eventsat some level. By setting priority values, managers can express relativetrade-offs for enforcement.

Zones and beats—geographically-bounded areas of a city where parkingenforcement officers perform their duty shifts while on patrol.

Super-beats—a parking enforcement region in a city that is larger than aregular beat. Typically, a super-beat combines several regular-sizedbeats and is staffed by several parking enforcement officers who operatecollaboratively as a team to carry out revenue-producing parkingenforcement enforcement activities and public safety-promoting serviceactivities.

Activity plan—recommends duty activities to a parking enforcementofficer at either an abstract or detailed level in terms of “what,”“where,” and “when.” An officer may be judged by whether he adheres toor departs from an activity plan without good reason. An activity plancan be presented textually or displayed visually through annotated maps.Descriptions involve three things, space (region), time (a timeinterval), and activities (violations to cite or services to perform).Descriptions can be at an abstract level by identifying regions that areneighborhoods or quadrants of a beat, or at a detailed level byspecifying specific directions and locations to patrol at one or more ofa street- or block-face level, for instance, patrolling on the left sideof Main Street between 4th and 5^(th) Avenues. Similarly, descriptionscan be at an abstract level by providing timing based upon periods ofthe day, such as which activities ought to be performed in the morningor afternoon, or at a detailed level by providing timings in an orderedspecific fashion, by which activities ought to be performed before otheractivities, or by which activities to perform during specific timeslots, for instance, between 3 p.m. and 4 p.m. Activity descriptionswithin an activity plan can include patrolling, expectations ofcitations to be found, services to be performed, and so forth. Thus, theactivity plan is not just a minute-by-minute schedule; rather theactivity plan can be an abstract plan with constraints, such asrequiring the officer to complete a task by a particular time of day.

Duty shift—a period of time when a parking enforcement officer works. Atypical duty shift is eight hours long, which includes time for patrol,duty activities, and breaks.

Hard and soft constraints. Hard constraints are constraints that must besatisfied in any proposed solution. Soft constraints can have associated“costs” for violating them, so that solutions that minimize the cost ofviolated constraints are preferred, although solutions that satisfy softconstraints are preferred over solutions that do not satisfy softconstraints.

Parking meters, parking kiosks and vehicle occupancy sensors. Parkingmeters and parking kiosks are city-managed curbside devices that allow amotorist to purchase the right to park a vehicle in a specific locationfor a limited amount of time; additional parking time must be purchasedupon the expiry of the meter, when permitted. Vehicle occupancy sensorsare sensors that determine whether vehicles are parked in parkingspaces.

ALPR-equipped vehicles. An ALPR-equipped vehicle are is a vehicle thatis equipped with an automatic license plate reader. Some readers canread residential parking permits directly or are able to look upresidential parking permits from license plate numbers. Vehiclesequipped with ALPRs can drive down the street at normal speeds and readand record license plates. The location and time of day corresponding toeach vehicle identified is stored. Some ALPR-equipped vehicles can alertthe operator to certain types of violations, for instance, “scofflawvehicles” with several unpaid parking tickets. The stored records ofidentified vehicles can be sent to a centralized database and downloadedto other parking enforcement vehicles. ALPR-equipped vehicles areincreasingly being used to reduce the labor costs of enforcing parkingtime limits in areas that do not have vehicle occupancy sensors.

Foreword

The performance of parking enforcement organizations is optimized bycreating joint human and computer teams. The system and method describedin detail infra exploits differences in human versus computerprogram-implemented cognition and accessible information to create suchhigh performing human and computer program teams. The following sectionsdescribe salient characteristics of human cognition, considerations forefficient teaming through modeling and coordination, and an analysis forcombining complementary computer and human capabilities.

Both humans and computers use cognitive models to guide their reasoning.People learn from practice. With practice, performance improves andresponse time decreases. The effect is called the Power Law of Practice.The curve relating performance to time and repetition is called alearning curve. The actual results depend upon the characteristics ofthe task being mastered, but generally, the power law of practice statesthat the logarithm of the reaction time for a particular task decreaseslinearly with the logarithm of the number of practice trials taken.Using the power law of practice, differences in performance betweenjunior parking enforcement officers and seasoned parking enforcementofficers can be predicted. The system and method described herein canuse these differences, for example, to advise officers depending uponthe context and their experience through the dynamic route predictor 74component of the planner and recommender layer 62, as further discussedinfra with reference to FIG. 4.

Human performance also depends upon several factors. For example, whenpeople perform multiple tasks (“multitask”) at the same time, they aremore likely to forget things and to make mistakes. Conversely, when theyfocus intensely on just one thing, they have little attention left forother things and can lose track of time. Target fixation refers to aphenomenon observed in airplane pilots who become so focused on a targetwhen diving their aircraft that they crash by having overestimated howmuch time they have left to pull up. Here, parking enforcement officerspatrolling a city multitask between driving, activity and routeplanning, and looking for parking violations. Depending upon thesituation, officers predictably exhibit diminished performance relatedto multitasking, target fixation, and stress. The system and methoddescribed herein can adjust amounts and types of advice according toestimated levels of priority and multitasking through the opportunitypredictor 75 component of the planner and recommender layer 62, asfurther discussed infra with reference to FIG. 4.

Traditionally, parking enforcement officers work mostly alone with eachofficer assigned to a separate beat. Each officer travels mainly withinhis assigned beat, unless he is working on a special assignment.Although this divide-and-conquer type of approach keeps officers out ofeach other's way, achieving optimal overall performance is precluded.For example, the traditional individual officer approach does not coversituations in which there are multiple high-priority needs within asingle beat at the same time.

For optimal overall performance, parking enforcement officers should beable to dynamically be deployed where they are most needed, irrespectiveof beat boundaries. The system and method described herein can predictand evaluate where officers are most needed and can keep officers awareof overlapping work done by other officers through the resourceallocator 75 component of the planner and recommender layer 62, asfurther discussed infra with reference to FIG. 4, so that the officersdo not waste time traveling needlessly to try to do work that hasalready been done.

In addition, supervisors are supported in making optimal plans ahead oftime, while dispatchers are assisted with managing reassignments duringthe course of a duty day to optimally handle unplanned events andemergencies. The status of activities and workload are tracked and usedto model the organization's overall needs dynamically to thereby findoptimal responses and recommendations. One approach to optimization usesmulti-officer “super-beats.” Suppose that a parking enforcement officeron a super-beat team (“super-team”) needs to respond to an event whileleaving their current work unfinished. The system can recommendrebalancing the assignments among other officers to minimize the loss ofrevenue through the coverage planner 78 component of the planner andrecommender layer 62, as further discussed infra with reference to FIG.4.

High performance teams of parking enforcement personnel need tocoordinate and collaborate. Coordination is the timing andsynchronization of actions for effectiveness. Collaboration refers towork where different people carry out different parts of a joint task.Xenospection refers to the practice of observing and understanding thework of others. Coordination and collaboration require communication. Tocollaborate, people engage in xenospection and need to understandactivity being shared. This understanding enables them to predict whencertain steps will be completed, to identify when help is needed, and todetect when someone is in trouble.

Conventionally, communication creates attentional overhead, as peopleneed to direct their attention to understand and respond tocommunications. The system and method described herein can provideautonomous communications capabilities to support coordination andcollaboration using computers as virtual partners in parking enforcementactivities. For example, the computer partner of each parkingenforcement officer has a model of the officer's assignments, prioritiesand the state of their duty activities and tasks. These models enablethe computer partner to handle some of the communications directed tothe officer without interrupting the officer in the performance of hisduties or requiring the officer's actual involvement in messagingbetween the computer partner and a centralized system that mediatesparking enforcement operations. This capability is called “conditionalautonomous messaging,” which is provided through the message director 71and conditional autonomous messaging 73 components of the computerpartner and communications layer 63, as further discussed infra withreference to FIG. 4.

Due to differences in their respective cognitive architectures, humansand specialized computers typically have different capabilities and alsodifferent blind spots and failure modes. Cognitive models frompsychology, such as described in D. Kahneman, “Thinking, Fast and Slow,”Farrar, Straus and Giroux (2011), the disclosure of which isincorporated by reference, help to explain the differences. Consider anartificial intelligence computer system that is designed to solveproblems or recommend solutions. Such a computer system is programmed tocarry out symbolic searches over a space of candidate solutions. Forexample, a chess playing computer system maintains a model of a virtualchess world with a representation of a chessboard, a rule basespecifying the permissible moves of the game, and state representing thecurrent status of gameplay in terms of the current positions of thechess pieces and moves having been played. The computer system playschess by manipulating simulated pieces on a virtual game board,evaluating the results of the simulated gameplay, and presentinggameplay recommendations.

Per Kahneman, cited supra, such systematic step-by-step cognitioncorresponds to System 2 thinking for humans and is characterized as theslow and careful part of human thinking that is invoked when the fastpart of human cognition, System 1 thinking, fails. This style ofcognition is much faster and more systematic for computers than forhumans. A generate-and-test type of artificial intelligence computersystem for symbolic problem solving shares many of the same qualities ofSystem 2 thinking in that the approach taken is deliberate andsystematic. However, generate-and-test machines can be blindsided whenthere are real world facts or constraints that are not represented inits world model.

Humans use System 1 thinking, “intuitive” thinking, for most cognition.System 1 thinking is a memory-based style of associative thinking, wherea major part of cognition is working from previous experiences.Intuitive thinking returns relevant information from memories aboutsimilar events from the past. The machinery of System 1 thinkingincludes ways of storing memories of experience and ways of retrievingmemories, given features or relationships. The workings of System 1thinking help to explain how chess masters can walk down an aisle in achess tournament, glance at ongoing chess games, and correctly predictan outcome, such as a “checkmate in five moves.” Tens of thousands ofhours playing chess create the experience to be able to recognizepatterns of play and immediately recognize an answer, without all of thestep-by-step work of a System 2 thinking-style approach.

System 1 thinking also has known biases in its logic. For example, whenpeople are asked to make predictions of what might happen based uponpast experience, they recall memories to mind and reflect on them. Thisprocess is known to create biases because the recalled memories tend tobe those memories that are most memorable, as opposed to other memories.This bias means that the recalled memories of experiences do not providenot a good sample. Memory bias distorts the ability to make validpredictions.

In humans, System 1 thinking draws on all sorts of varied experiences.Although not strictly logical, System 1 provides a great repository ofwhat is colloquially called, “common sense.” Where System 2 thinking islogical but narrow, System 1 thinking is illogical but general. System 1thinking does not use a model to predict how things work and simplymemorizes what happens. Thus, System 1 thinking finds the closestmatching memory and may return the wrong answer or the answer to thewrong problem.

Computer programs have definite advantages of speed for many kinds ofinformation processing and are also much faster at mining largedatabases of potentially relevant information to uncover correlations indata and to create predictive models. In freestyle human versus computerchess games, the chess programs operate in ways analogous to System 2thinking by reasoning step-by-step. However, computer programs also haveweaknesses. Computer programs are programmed to use certainabstractions, representations and reasoning methods and generally makemistakes on “open world” problems when the abstractions,representations, and reasoning methods fail to account for somethingabout that open world.

These human versus computer chess examples suggest possibilities forsynergistically uniting the capabilities of humans and computer programsto combine different forms of cognition and different kinds ofinformation. In the chess game example, the humans provide oversight andattention management for the computers as they search the game solutiontree. In open world settings, humans also have access to different kindsof information based upon life experience. For example, whereas acomputer program can generate and present options efficiently, theultimate selection of a plan can often be improved by including a humanbecause the human has access to (System 1 thinking-based) common senseand real world knowledge not available to the computer. On the otherhand, a computer program can potentially make findings that a humanwould be unable to make based upon the computer program's ability tobuild statistical models from large databases. Further, humans makepredictable errors when they need to reason in a hurry or aremultitasking. In such cases and analogous to freestyle chess, a computerprogram can readily spot errors or violated constraints and find optimalsolutions because they are systematic and tireless.

Architecture

During practically every hour of every day, countless operationaldecisions are made by the individuals working together as part of aparking enforcement organization, whether managers, supervisors,dispatchers, parking enforcement officers, or other personnel in theorganization. FIG. 1 is an organizational chart showing, by way ofexample, the managerial hierarchy 10 of a parking enforcementorganization. At all levels of the organizational hierarchy, parkingenforcement personnel are interlinked through virtual computer partners(“c-partners”). These c-partners communicate with their human partners(“h-partners”) and with each other to support coordinated parkingenforcement teams. Typically, a parking enforcement organization'smanagerial hierarchy 10 includes managers 11, supervisors 12 anddispatchers 13, and parking enforcement officers 14, although mostorganizations also include administrative and support personnel.

Managers 11 (or captains) are responsible for organizational performanceover an entire city or region and are mainly concerned with formulatingpolicy, identifying long-term trends and setting priorities. In largecities, management may be divided into multiple levels with one or moreassistant managers (or lieutenants) and can include dedicated analysts.

Supervisors 12 (or sergeants) oversee the performance of their squads,including assigning individual parking enforcement officers to beats andservice tasks and reviewing officer performance. Supervisors 12 are alsoresponsible for balancing workload between planned events, such astraffic control, and ongoing enforcement activities, such as parkingenforcement.

Dispatchers 13 are primarily responsible for supporting public safety byorchestrating responses to unplanned events reported through emergencyservices, such as 9-1-1 and 3-1-1 calls, or other communicationschannels. Dispatchers 13 are expected to quickly identify the bestpersonnel to deploy and to monitor the execution of tasks whileassigning or freeing resources, as appropriate.

Parking enforcement officers 14 are deployed on the streets to carry outtheir duties along their respective beats. Their duties fall into twomain groups. First, officers 14 carry out planned events and performenforcement activities, including issuing citations (tickets) orwarnings to enforce parking regulations. Second, officers 14 carry outunplanned events and perform service activities, such as directingtraffic at public events, school drop-off and pick-up, sports games,fires, accidents, and so on, in response to dispatch. In addition, theyreport their on-the-scene observations to dispatch and receiveinformation that helps them to plan and carry out their work.

Parking enforcement officers 14 on the beat enforce the city's parkingregulations, direct traffic and ensure public safety. FIG. 2 is a blockdiagram showing a system 20 for monitoring parking enforcement officerperformance with the aid of a digital computer in accordance with oneembodiment. For simplicity, parking, whether controlled by parkingmeters or kiosks, posted signage, or city ordinances, and regardless ofwhether curbside, within a parking lot, or in other physical locations,will henceforth be called “parking.”

While on duty, officers 14 remain in remote wireless communications withtheir supervisors 12, dispatchers 13, fellow officers 14, and otherpersonnel within the parking enforcement organization throughconventional forms of communications (not shown), including radio- andcellular phone-types of devices. In addition, a suite of parkingenforcement support services 22 is provided in part through one or moreservers 21, which are located over a digital communications network thatis wireless-capable. The specific modules of the parking enforcementsupport services 22 will be discussed in detail infra.

Supervisors 12, dispatchers 13, officers 14, and other personnelcommunicate and remotely interface with the parking enforcement supportservices server 21 over the network using mobile devices that includewirelessly-connectable digital computing devices 25, such as personal,notebook and tablet computers, and so-called “smart” mobile computingdevices 26, such as smartphones and the like. Still other types ofcommunications and remote interfacing devices are possible. Thesupervisors 12, dispatchers 13, managers, and other personnel who aretypically located in situ in the organization's physical office spaceslikewise interface with the parking enforcement support services server21 over the network using the same types of wireless digital computingdevices, albeit without the continual movement on the streets asoccasioned by officers 14 in the performance of their duties, throughwired digital computing devices, or both. The digital computing devices,whether wireless or wired, constitute the c-partners of the parkingenforcement personnel.

Physically, the wireless digital computing devices may be integratedinto the officers' patrol vehicles, if applicable, or could be discretestandalone computing devices. As well, locational data, such asgeolocation coordinates, are sensed and continually relayed to theparking enforcement support services server 21; the locational data canbe provided through GPS, Wi-Fi address tables, or other location sensingdevices, either integrated into the officers' wireless digital computingor devices patrol vehicles, or through dedicated GPS or similarreceivers. Still other ways to sense and relay the officers' locationaldata are possible.

The servers 21, personal, notebook and tablet computers 25, mobiledevices 26 can each include one or more modules for carrying out theembodiments disclosed herein. The modules can be implemented as acomputer program or procedure written as source code in a conventionalprogramming language and is presented for execution by the centralprocessing unit as object or byte code. Alternatively, the modules couldalso be implemented in hardware, either as integrated circuitry orburned into read-only memory components, and each of the client andserver can act as a specialized computer. For instance, when the modulesare implemented as hardware, that particular hardware is specialized toperform the data quality assessment and other computers cannot be used.Additionally, when the modules are burned into read-only memorycomponents, the computer storing the read-only memory becomesspecialized to perform the data quality assessment that other computerscannot. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium, such as afloppy disk, hard drive, digital video disk (DVD), random access memory(RAM), read-only memory (ROM) and similar storage mediums. Other typesof modules and module functions are possible, as well as other physicalhardware components.

If a patrol vehicle 27 is so equipped, an officer's ALPR 30 will readthe license plates and record the locations of parked vehicles along theofficer's patrol route within a beat. The ALPR 30 interfaces with theparking enforcement support services server 21 to create records of thelicense plates of vehicles scanned as the officer 14 drives by; in turn,the server 21 will recognize whether the vehicle corresponding to thelicense plate is parked in a manner in violation of applicable parkingregulations, such as over-time or abandoned parking. For instance, ifthe same vehicle's license plate is scanned by the ALPR 30 twice and thevehicle's location has not changed, the vehicle could be in violation ofparking regulations if the vehicle's parking location is subject to atime restriction. The officer 14 can then be alerted to the violation bythe parking enforcement support services server 21 and the officer 14could issue a citation or warning.

Within the city, individual parking meters 23 and centralized parkingkiosks 29 may be provided to allow a motorist to purchase the right topark a vehicle in a specific location for a limited amount of time;additional parking time must be purchased upon the expiry of the parkingmeter 23 or parking kiosk 29, when permitted. Parking meters 23 aregenerally paired with a specific parking space, whereas parking kiosks29 may cover a range of parking spaces, such as along a block face. Theparking meters 23 and parking kiosks 29 can be remotely connected overthe network to the parking enforcement support services server 22, orboth, whether wirelessly or wired, which can use the parking meterpayment data in track expired parking and, when paired with vehicleoccupancy sensors 24, 28, discussed below, to identify parkingviolations that can be provided to officers 14 through their mobilecomputing devices.

Other types of sensors can help create a comprehensive picture of thestreets from a parking enforcement perspective. For instance, one ormore of the parking spaces may be equipped with vehicle occupancysensors 24, 28 that determine whether the parking space is occupied by amotor vehicle. Typically, these sensors are magnetic field sensorsembedded in the street, but fixed and mobile video cameras, licenseplate readers, and other similar kinds of sensing devices can also beused to detect vehicles, read license plates and otherwise determinethat a parking space is occupied or vacant. The vehicle occupancysensors 24, 28 can be directly interfaced with a parking meter 23 orparking kiosk 29, remotely connected over the network to the parkingenforcement support services server 22, or both, whether wirelessly orwired. As well, camera sensors 31 posted on the streets can supplementthe vehicle occupancy sensors 24, 28 and ALPRs 30 to track where andwhat vehicles are parked and at what times of the day, as well asproviding electronic “eyes” on the streets that can be used bydispatchers 13, supervisors 12, and other personnel who need to seeon-the-street conditions in real time, such as traffic flow or how aresponse to an emergency is progressing. Finally, information iscontinually gathered from other types of sensors, including locationaldata from GPS receivers or Wi-Fi transceivers in officers' vehicles ormobile computing devices, traffic loop sensors, parking meter paymentdata from parking meters 23 and parking kiosks 29, car markinginformation from ALPR-equipped vehicles 27, camera sensors 31, andtraffic flow sensor information.

The parking enforcement support services 22, parking meters 23 andparking kiosks 29, vehicle occupancy sensors 24, 28, ALPR 27, camerasensors 31, and other deployed parking regulation enforcement devicesand sensors can implement network security protocols to ensure securecommunications. As necessary, different secure communications schemesand levels can be applied over all communications. For example, publickey cryptography could be used in various secure protocols to protectcommunications between all system elements.

Most situations that the parking enforcement organization's personnelencounter each day are complicated and making decisions that are optimalis difficult because time is short and relevant data is not alwaysavailable to every decision maker involved. In addition, fulfilling thecompeting needs of promoting public safety (through service activities)and producing revenue (through enforcement activities) both require thatwork be performed by the same set of people, that is, the parkingenforcement officers 14, at the same time. Under some circumstances,this type of competition for limited resources can force an organizationto have to divide its efforts between revenue-producing compliance(ticketing) activities and non-revenue-producing service (public safety)activities in ways that are ineffective and suboptimal.

These concerns can be resolved by providing parking enforcementorganization personnel with real time information coupled withoperational managerial and situation assessment tools to therebyfacilitate optimal performance at all levels of the organization. Thec-partners have access to elements that provide information services,such as monitoring, alerting, planning, and recommending. Theorganization's personnel are thus empowered with the information andtools necessary to making the decisions that guide the organization'sactivities in an optimal fashion.

The needs of the personnel manning a parking enforcement organizationcan be effectively met by creating fast OODA (Observe-Orient-Decide-Act)loops that are more comprehensive, data-driven, and faster than what iscurrently available to parking enforcement personnel throughconventional approaches and which exploit the complementary capabilitiesof human and computer cognition in an empowering and synergisticfashion. FIG. 3 is a functional block diagram showing a method 40 formonitoring parking enforcement officer performance with the aid of adigital computer in accordance with one embodiment. Parts of the method40 is implemented in software and execution of the salient portions ofthe software is performed as a series of process or method modules orsteps by a combination of computer hardware, including servers 21,personal, notebook and tablet computers 25, mobile computing devices 26,communications network infrastructure, parking meters 23 and kiosks 29,vehicle occupancy sensors 24, 28, ALPR 27, and other kinds of sensors,including GPS receivers in officers' vehicles or mobile computingdevices, traffic loop sensors, and traffic flow sensors, as describedinfra.

By way of overview, the method 40 can be divided into three functionalfacets, user interaction systems 41, planning, modeling and memorycomponents 42, and goal and policies tools 43, all of which rely, tosome degree, on information obtained through data collection sources 54.These three functional facets are revisited in depth infra withreference to FIG. 4. The user interaction systems 41 provide interactiveuser interfaces that serve as windows into the system 20 for the parkingenforcement organization's personnel, including managers 11 (managerinterface 44), supervisors 12 (supervisor interface 45), dispatchers 13(dispatcher interface 46), and parking enforcement officers 14 (officerinterface 47). The user interfaces may be hosted on a traditionalpersonal or notebook computer 25, such as would be used in an officeenvironment, or on a mobile computing device 26, such as a tabletcomputer or smartphone with which a parking enforcement officer may beequipped while out on patrol. Still other user interfaces and types ofuser interface hosting platforms are possible.

The planning, modeling and memory components 42 includes an activityplanner and recommender 48 that generates activity plans of upcomingduty activity recommendations for parking enforcement officers 14,including predicting enforcement activities along beats and super-beatsand service activities that are expected to be performed. In determiningrecommendations for upcoming duty activities, the activity planner andrecommender 48 considers inputs from a modeler for parking, traffic,events, and activities 49 that uses a time-based active representationalmodel for a city situation, enforcement activities, estimates of parkingviolations expected to occur within each beat, and city enforcementpolicies and priorities stored in a dynamic database 50. The planning,modeling and memory components 42 are typically hosted centrally on theparking enforcement support services server 21. Still other planning,modeling and memory components are possible.

Finally, the goal and policies tools 43 empower managers 11 and otherpersonnel within an organization with the ability to implementhigher-level goal and policy abstractions into practicable solutions. Amotivation recommender tool 51 provides a real time system formotivating officers 14 and supervisors 12 through motivation touchpoints and different types of motivations. A coverage, compliance andsaturated enforcement recommender tool 52 assists managers 11 andsupervisors 12 with planning coverage in under-covered regions,maintains models of elastic response, and improves statistical knowledgeof the “ground truth” of a beat, that is, the actual level ofnon-compliance. Last, a super-beat and super-team models tool 23 enablessupervisors 12 to model the coverage and overall effect of formingdifferent combinations of beats into super-beats and individual parkingenforcement officers 14 into super-teams. The goal and policies tools 43are also typically hosted centrally on the parking enforcement supportservices server 21. Still other goal and polices tools are possible.

The specific modules of the parking enforcement support services 22 arestructured into five layers. FIG. 4 is a block diagram showing thearchitectural elements 60 of the parking enforcement support services 22as implemented on the parking enforcement support services server 21 ofFIG. 2. By way of overview, the user interaction layer 61 providesinteractive user interfaces that serve as windows into the system 20 forthe parking enforcement organization's personnel. The planner andrecommender layer 62 generates upcoming activity recommendations forparking enforcement officers by predicting duty activities along patrolroutes within a beat. The computer partner and communications layer 63handles communications vetting, including conditional autonomousmessaging on behalf of their human partner, that is, the officer 14paired with the computer partner. The database and model layer 64 storesa time-based active representational model 83 for the city andhistorical data 84, including officer performance, citations andwarnings, and past planned and unplanned events. Finally, the monitoringsystem layer 65 provides dynamic oversight over events and statechanges. The layers will now be discussed in detail.

The user interaction layer 61 is implemented through the userinteraction systems 41 discussed supra with reference to FIG. 3 andincludes two components. First, an officer activity interface 69presents optimal patrol routes within a beat to officers 67, as well assupervisors and dispatchers 68, as further discussed infra withreference to FIGS. 13-21. Second, a situation assessment interface 70provides the status of on-going events, officer whereabouts, alerts,communications, and real-time feeds for use by supervisors anddispatchers 68, as well as officers 67, as further discussed infra withreference to FIGS. 47-49. Other user interaction layer components arepossible.

The planner and recommender layer 62 generates upcoming activityrecommendations for parking enforcement officers. The planner andrecommender layer 62 utilizes information from the time-based activerepresentational model 83 in the database and model layer 64 andincludes six components, a dynamic route predictor 74, an opportunitypredictor 75, a resource allocator 76, a response plan generator 77, acoverage planner 78, and a motivation recommender 79. Other planner andrecommender layer components are possible.

Here, one or more activity plans are built by the system for eachofficer using the dynamic route predictor 74 and the opportunitypredictor 75 in the planner and recommender layer 62. There could bemultiple optimal activity plans where the differences between thecompeting plans are insubstantial or statistically irrelevant. In thatsituation, the competing optimal plans could be presented to theofficer, who can then choose one of the activity plans, or the system 20can dynamically pick one of the activity plans for the officer.

The dynamic route planner 74 and the opportunity predictor 75 buildactivity plans that optimize officer performance. An officer'sperformance is multi-dimensional because there are multiple demands onthe officer 14 and trade-offs need to be made in deciding what to do. Anofficer 14 is expected to enforce the most important citations, meetservice demands, take required breaks, and so on. These differentactivities compete with each other. Using time effectively is generallypart of the requirement, and so is doing the most important things asdetermined by departmental policies.

The problem of identifying those activity plans that optimize officerperformance can be modeled as a constrained optimization problem withpriorities. The computation can factor in the time taken to walk ordrive a block, the number of blocks to be enforced, the time spentissuing the number of predicted citations, and other considerations thatcan affect time to enforce. The opportunity predictor 75 component ofthe planner and recommender layer 62 builds an activity plan thatoptimizes patrol routes within a beat based upon productivity, asfurther discussed infra. Patrol routes within a beat for officers 67 areperformance-optimized based upon anticipated productivity by calculatingthe anticipated number of parking violations per block, with resourcesdeployed to maximize citation issuance. The correlation between theaverage time that a vehicle is parked and the maximum time that vehiclesare allowed to park in each metered parking space is a keyconsideration.

In a further embodiment, an optimal activity plan could be generated bythe components of the planner and recommender layer 62 that has beenoptimized for given or expected conditions. The optimization processtake into account all available information, including city ordepartment policies, the need for breaks, information about what otherofficers 14 have already done, expectations for violations (andcitations) and markings, traffic conditions, sensor data, historictrends and known factors, details about the shift (time of day orperiod, such as morning, afternoon, or graveyard), day of week, numberof officers 14 present, and known service requests with policies used toprioritize across competing needs. Based upon the foregoing factors, oneor more activity plans, including predictions of expected performance,are generated and metrics are applied to the predictions of expectedperformance. The best performing plan, or plans, are selected, whichconstitute an optimal activity plan. A version of the optimal activityplan, possibly in abstract form, is presented to supervisors 12 andofficers 14, together with the predicted expectations and relevant data.Still other ways of computing optimal activity plans are possible.

Three of the components of the planner and recommender layer 62, theresource allocator 76, the response plan generator 77, and the coverageplanner 78, are focused on providing support to supervisors anddispatchers 68, as further discussed infra with reference to Scenarios 3and 4. The resource allocator 76 identifies the resources needed tohandle unplanned events. The response plan generator 77 creates theoperational plans for the teams and recommends adjustments to theoperational plans throughout the day as the situations change. Thecoverage planner 78 rebalances the remaining activities among theparking enforcement officers remaining on a team after one or more ofthe officers are assigned to handle an unplanned event.

Finally, a motivation recommender 79 generates performance analytics forpresentation to supervisors and dispatchers 68 and parking enforcementofficers 67, as well as to managers, as further discussed infra in thesection entitled, Performance and Motivational Analytics. The motivationrecommender 79 generates analytics about working hard and working smartthat draw on models of urban situations and which predict expectationsof best choices in activities. The motivation recommender 79 createsperformance analytics to cover three broad times, before an activity isdone, after an activity is done, and while an activity is being done.The analytics help an officer, his supervisor, or other personnel answerthe questions, given the present state of the city, what is expected ofthe officer in terms of performance and what should the officertherefore be doing while on the beat? Thus, the performance analyticsreflect what an officer was, is or should be doing, depending uponwhether the analytics respectively are retrospective, real time orprospective, the service time taken by the officer to issue citations,citations or warnings actually issued, the officer's movements (or lackthereof), circumstances that might affect the officer's performance, andother factors that are specific to the moment of inquiry.

The computer partner and communications layer 63 handles communicationsvetting between the system 20 and the officers 67 and supervisors anddispatchers 68. First, a message director 71 dynamically routes messagesbetween the organization's personnel, particularly where tagged messagesare utilized, as further discussed infra with reference to FIG. 4. Theobserver 72 monitors the whereabouts and activities of officers 67 andgenerates alerts when a deviation from a recommendation or operationalplan is identified, also as further discussed infra with reference toFIG. 4. The whereabouts of officers can be determined by the observer 72directly based upon locational data provided through GPS, Wi-Fi addresstables, or other location sensing devices, either integrated into theofficers' wireless digital computing or devices patrol vehicles, orthrough dedicated GPS or similar receivers, or indirectly throughtraffic loop sensors, car marking information from ALPR-equippedvehicles, traffic flow sensor, or camera sensors 31. Finally, aconditional autonomous messaging 73 component provides conditionalautonomous messaging on behalf of a human partner, that is, an officer14, who is paired with the computer “partner,” as further discussedinfra in the section entitled, Balancing Work Load and Handling anUnplanned Event. Other computer partner and communications layercomponents are possible.

The database and model layer 64 stores a time-based activerepresentational model 83 for the city that fuses historical and currentparking citation data with information received from sensors throughoutthe city and includes estimates of parking violations expected to occurwithin each beat. The “active representational” aspect refers topersistent memory and computational processes that represent the current“state information” about the city and about the activities of theparking enforcement organization, particularly the on-the-beatactivities of the officers and the enforcement activities that theyperform. The active representational model 83 combines information basedupon recent sensor data, including locational data from GPS receivers inofficers' vehicles or mobile computing devices (or determined throughWi-Fi triangulation or other location-sensing devices), traffic loopsensors, parking meter payment data, car marking information fromALPR-equipped vehicles, and traffic flow sensor information, as well asduty status and parking citation information from officers. They alsouse historical data 84 in the modeling, including historical citationdata that reflects past parking violations within each beat, along withthe time of day and day of week for the violations. The database andmodel layer 64 is time-oriented by making predictions about what ishappening at different times. Some of the predictions are alsospace-oriented, in that these predictions predict what will happen (orwill happen in the aggregate) in regions of different sizes in the city.

The active representational model 83 includes algorithms that predictparking violations in regions, for instance, neighborhoods, and times,for instance, during a particular hour on a given day of the week. Themodeling takes into account seasonal effects, day of week, neighborhoodtrends, and recent enforcement history. In addition, the modeling cantake into account known ongoing factors, such as parades, weather,traffic, accidents, fires, sporting events, holidays, and so on. In oneform, the algorithms can predict the probabilities or likelihoods ofviolations in parts of a city based upon historical data, includingspatially sampled or temporally sampled historical citation data.

In a further embodiment, the algorithms can include the use of a widerrange of factors in addition to (or in place of) historical citationdata to create different predictive models for different kinds ofneighborhoods. For instance, additional factors can include:

-   -   Parking behaviors, such as double-parking in commercial loading        zones.    -   Day of week combined with time of day.    -   Categories of neighborhoods, such as residential, commercial or        business, industrial, or mixed use.    -   characteristics of neighborhoods, such as a residential        neighborhood near to a business district, hospital, or        university, into which motorists who are unable to find parking        will overflow.        These additional factors can be used in the training of the        parameters of the predictive models as an adjunct to historical        citation data or, if historical citation data is not available        (or is unreliable), as a surrogate for historical citation data.        Still other additional factors are possible.

The active representational model 83 exports third party applicationprogramming interfaces (APIs) 66, including a traffic API 85, for systemelements making inquiries for questions about the current state. In oneembodiment, a publish and subscribe approach is employed, whereprocesses can publish data to make the data available to other processeswhen the data becomes known. In this approach, data is “pushed” or sentas the data becomes available to subscribing processes that haveregistered to receive the data. In a further embodiment, data can be“pulled” or provided upon request, which enables system processes to“pull” or query the active representation model 83 when they need thedata.

Data that can be computed or provided in the active representationalmodel 83 includes:

-   -   Locations, movements, and current tasks and duty statuses of        officers on duty.    -   Life cycle of violations, for instance, where violations are        known to have occurred, where violations are predicted to occur,        numbers, time before compliance ends, which violations have been        cited, which violations are likely to be cited.    -   Life cycle of events, for instance, which events are active,        current state of active events, staffing levels, time before        finishing or reaching next stage of events, participants.        Other types of computed or provided data in the active        representational model 83 are possible.

The system 20 fuses information from many sources, including sensorsdistributed around the city with current and historical citation data,to inform its active representational model 83 for the city and tocreate an estimation of the current situation. For example, sensorinformation could come from fixed sensors in the road, ALPR sensingsystems on patrol cars, buses, delivery vehicles, and other vehicles,vehicle occupancy sensors associated with parking spaces, parkingpayment collectors, and other sensors fixed or movable in the region. Inaddition, information about traffic, parking place occupancy, andongoing citations can be collected. Elements of the system 20 areresponsible for collecting and storing this information, and revisingestimates about the urban situation, and updating the status informationand status information in the active representational model 83 for thecity, as discussed supra.

The status information in the active representational model 83 for thecity are used to update predictions and make recommendations through theofficer activity interface 69 and situation assessment interface 70, aswell as in other contexts in which the information might have a bearingon decisions being considered or made. For example, information from thevehicle occupancy sensors 24, 28 is used to update expectations ofparking regulation violations and information from ALPR-equippedvehicles is used to update information about when the vehicles that arecurrently parked in time-regulated parking zones will become subject tocitation.

The monitoring system layer 65 provides dynamic oversight over eventsand state changes in the urban environment of the city and oversight ofthe activities of the parking enforcement officers 67. This layer fusesinformation coming from the computer partner and communications layer 63and provides the information to the active representational model 83 inthe database and model layer 64 for updates. The monitoring system later65 also maintains dynamic vigilance of evolving situations andenforcement activities and provides alerts as needed to parkingenforcement personnel.

The monitoring system layer 65 includes three components. First, anevent monitor 80 monitors the status information of ongoing events, asfurther discussed infra in the section entitled, Adjusting Plans toRe-Allocate Activities as Needed. The event monitor 80 tracksevent-related data, such as the positions and roles of eventparticipants, relevant sensor data, and tagged message streams. Thisinformation is used to track transitions for the active representationalmodel 83. Second, a situation monitor 81 tracks performance andsituation indicators not necessarily tied to specific events. Thepurpose of the situation monitor 81 is to look for developing conditionsthat need attention. Finally, an officer monitor 82 tracks theperformance and activities of officers beyond their assigned eventactivities. On the one hand, officers 67 could have safety issues, suchas if they are involved in an accident or are approaching a dangeroussituation known to be dangerous. From a management perspective, officers67 on occasion could appear to be off their assigned beat, parkedsomewhere unexpected or in non-productive situations. The officermonitor 82 is intended to fuse relevant information from the officer'svehicle, the officer's mobile computing device 26, and potentially othersensors and to send alerts to the officer 14, a dispatcher 13 orsupervisor 12, as appropriate. Other monitoring system layer 65components are possible.

Performance and Motivational Analytics

This section provides a framework for understanding variations on howanalytics can be implemented into the system and how the variations onanalytics are based upon different hypotheses of how to improve oroptimize performance. Theoretically, there are three broad approachesabout what is most effective and sustainable in motivating performance:

Activity Alerts (for working hard). This approach uses monitoringsoftware to detect when officers are not being productive. The theory isthat low performance is caused by slacking or goofing off behaviors. Thehypothesis is that performance will improve if supervisors get real-timealerts about low-performing officers and coach them when slacking orgoofing off behaviors are happening.

Real-time Optimization Recommendations (for working smart). Thisapproach uses predictive analytics to create recommendations for optimalperformance. The theory is that low performance is caused by people notknowing what to do. The hypothesis is that by providing recommendationsjust in time, officers, dispatchers, and supervisors will be informed,make better choices and improve performance.

Motivational interfaces (for making jobs more interesting). Thisapproach is about making jobs more interesting. A body of psychologicalresearch on motivation and performance, such as described in D. H. Pink,“Drive: The Surprising Truth About What Motivates Us,” Riverhead Books(Apr. 5, 2011), the disclosure of which is incorporated by reference. Inthis approach, for any activity that involves even a little bit ofcognition, the greatest performance is found when the task provides asense of autonomy, mastery, and purpose.

These three approaches are complimentary designing and the system 20provides the tools that enable an organization to learn what works best.The motivational tools are configurable, so that the organization cantry different motivational strategies with their personnel and collectdata about performance.

Working Hard and Working Smart

The distinction between “working hard” versus “working smart”illuminates two broad theories about why organizations perform poorly.Analytics about working hard measure amounts of activity and rawproductivity numbers. Parking enforcement officers 14 on patrol arelargely out of sight of supervision. In some cities, interest in workinghard analytics grew over the last few years following news stories aboutofficers going home when they are supposed to be working or sleeping intheir vehicles for hours when they were on duty.

The theory behind working hard analytics is that officers are loafingand not performing their work and the situation causes low performance.Examples of simple working hard analytics include identifying how busypeople are. For instance, are there gaps of time when individuals haveno apparent output? Are those individuals just standing still,apparently doing nothing? Are those individuals underperforming comparedto the expectations for other individuals at comparable times andlocations based upon historical data or real time situation data?

The underlying analytics about working hard can be nuanced. Suchanalytics need to account reasonably well for the time of day, day ofweek, whether a parking enforcement officer 14 is parked because theyare performing a service responsibility, or are on break.

Analytics about working smart draw on models of urban situations andpredict expectations of best choices in activities. Supervisors 12,dispatchers 13, and parking enforcement officers 14 make decisions allday long about where to go and what to do. Interest in working smartanalytics is growing because organizations are increasinglyunderstanding that enforcement resources are not deployed optimally,which follows the idea that recommendations should be based upon datathrough a process that involves prioritizing, selecting, and checkingfeasibility.

The theory behind working smart analytics is that low performance iscaused by people focusing on the wrong things at the wrong times andplaces. Examples of working smart analytics combine measures of workflowwith measures of opportunity. For instance, are officers 14 using routesthat take them where they are most needed for productivity? Are officers14 ignoring recommendations and engaging in less productive activities,or adhering to following the recommendations? Are officers 14 aware thatanother officer 14 has passed through their beat and has already pickedup the available citations on a stretch of blocks? These concepts can bequantified into an activity plan for each officer 14. In a furtherembodiment, other factors can be taken into account, such ascharacteristics pertaining to the beat, including the nature of thebeat, time of day, day of week, season, number of officers on the beat,traffic conditions, and service requirements, and officer-specificfactors, including the officer's level of experience and familiaritywith the beat. Other characteristics and factors could also be applied.The officer's performance can be gauged against the activity plan undereither the rubrics of working hard, where no movement or activity mayindicate that the officer 14 is not doing his job or loafing off, orworking smart, where the officer's decision to not follow the activityplan may indicate bad choices in where, what and when to enforce parkinginfractions.

Working smart analytics also apply to higher levels in an organization.For example, are supervisors assigning their squad to the beats mostworth working? When dispatchers 13 interrupt parking enforcementofficers 14 while working, do the dispatchers 13 assign the wrong peopleand unnecessarily disrupt the productivity of the organization? Whensupervisors 12 interact with officers 14, do they pay enough attentionto low performers? When managers create organizational policies, do theyaccount for the impact of their policies on other dimensions oforganizational performance?

Predictive analytics for parking citations in a region model parking andviolation-related activities by region and time interval.Recommendations for which activities to choose can be computed bysystems that generate plans for different alternative activities andoutcomes, compare and evaluate the outcomes, and recommend the topchoices. Systems making such predictions and recommendations can usehistorical data and a raft of sensor information. For example, parkingoccupancy, as available through vehicle occupancy sensors, and trafficconditions are predictive of the amount of parking activity, theavailability and competition for parking spaces, and the number ofexpected violations. Thus, those areas within a beat situated about anofficer's general direction of travel within which expected parkingviolations in his activity plan are expected are identified andrecommendations for each of those areas are provided to the officerafter considering the officer's actual (tracked) activities and anycompliance activities (citations issued) already performed by theofficer, so as to avoid inefficiencies in re-patrolling areas too soonor visiting areas that are not likely to be productive.

Analytics and Presentation Timings

There are three broad times for presenting performance analytics in anenforcement organization: a preview presentation before an activity isdone, a review presentation after an activity is done, and a real timepresentation while an activity is being done. The theory of analyticimpact varies with the presentation timings.

Preview analytics are presented before an activity is done. For example,previews can take place at the beginning of a shift or at the beginningof working a part of a shift. The mobile interfaces for parkingenforcement officers 14, as illustrated in the scenarios, discussedinfra, provided previews for a shift (“Plan my Shift”) and orienting(“Plan for this Neighborhood”). The theory for preview analytics is thatthey prepare an officer 14 to organize their activities properly beforethey carry them out.

Review analytics are presented after an activity is done. Theseanalytics are the most common kind of analytics that are used. Reviewanalytics are a kind of post-mortem analysis. They can cover one orseveral sessions of performance and identify areas of low performance.The theory of review analytics is that they encourage reflection bypeople about what is going wrong and provide an opportunity to recognizehow to do better and commit to doing that.

Real time analytics are presented while an activity is being done. Forexample, alerts about working hard can take place when an excessive timegap is noted and before the time gap gets any longer. An activity orrouting recommendation can take place at the time of a decision. Thetheory for real time analytics is that they guide decisions when theyare being made, and that the time that decisions are being made is ahigh leverage point for improving performance.

Touch Points and Influence

There are four broad touch points for presenting performance analyticsin an enforcement organization, presentation to supervisors 12, todispatchers 13, to managers, and to parking enforcement officers 14. Thetheory of influence and effectiveness of an analytic varies with thetouch point.

Supervisors 12 have oversight over squads of officers 14. Typically,supervisors 12 are responsible for assigning officers 14 to beats,reviewing their officers' performance, and advising their officers 14 ontheir work. If supervisors 12 assign officers 14 to the wrong activitiesor fail to monitor the low performers in the organization, theperformance of the organization can be compromised. The theory ofanalytics for supervisors 12 is that supervisors 12 have a broad impactfor gating the performance of the organization. Supervisors 12 need arecommendation system to assign officers 14 optimally, giveninstitutional policies. Supervisors 12 also need oversight of lowperformers to encourage the low performers to work harder or worksmarter.

Dispatchers 13 have oversight over the real time unfolding of dailyactivities. Typically, dispatchers 13 take or receive 9-1-1 calls andother requests and decide which people or resources to assign to therequests. Today, dispatchers 13 typically have little or no data aboutthe impact that choosing particular officers 14 might have on citationperformance (revenue). In some organizations, dispatchers 13 put out acall for assistance and take the first parking enforcement officer 14 torespond. Dispatchers 13 generally have little to no responsibility forrebalancing other responsibilities after an officer 14 is pulled fromwhatever he is doing. The theory of analytics for dispatchers 13 is thatdispatchers 13 have the potential to disrupt or preserve and balance thecitation performance of an organization. Dispatchers 13 need amonitoring interface, situation awareness, and a recommendation systemto effectively wield their influence on ongoing activities.

Parking enforcement officers 14 have oversight of their own activities.Typically, officers 14 have no visibility for events outside theirimmediate location. Officers 14 are often unaware of any activities of adifferent officer 14 that passes through their beat. In mostdepartments, officers 14 also do not have any data for optimizing theirchoices, including “best case” awareness, where everything goes asplanned, or in dynamic situations, where data of ongoing events becomesavailable. The theory of analytics for officers 14 is they need bothsituation awareness and recommendations about optimal choices to dotheir jobs well.

The notion of a patrol “beat” will now be discussed.

Beats

Depending upon the vernacular chosen by a parking enforcementorganization, the geographic areas within a city where parkingenforcement officers 14 carry out their parking enforcement activitiesmay be called zones or beats. These terms will be used interchangeably,unless otherwise noted. Beats are used to organize and divide the workof the officers 14. Traditionally, a medium-sized city may be dividedinto twenty or thirty beats with a single parking enforcement officer 14assigned to patrol each beat during a shift. Typically, there aredifferent beats for daytime and nighttime. At night, less parking andtraffic enforcement is needed than during the day, and parkingenforcement officers 14 can therefore cover larger areas and focus ondifferent kinds of violations at night.

In the traditional beat approach, one parking enforcement officer 14 isassigned to each beat. This approach keeps officers 14 out of eachother's way and provides clear accountability for coverage of eachneighborhood or block of a city. Traditionally, beat design wasconcerned mostly with the amount of area that an officer could covereffectively during a shift. In most cities, beats are mapped out andupdated every few years.

The infrequent design of beats every few years works best when theregions being covered do not change in character and when the size ofthe parking enforcement force is fairly constant. These assumptionsbreak down in practice, either because neighborhoods evolve and havedifferent enforcement needs or because the enforcement force changes insize. To account for such changes, traditional, fixed beat can beredesigned as often as needed as conditions change. In principle, beatscould be re-designed every day or even during a single day, such as whenparking enforcement officers 14 become unavailable due to unexpectedcircumstances. When resources are limited, beats could be designed toenforce areas with the highest probability of citations. For example,when there are constraints on time or staffing, parking enforcementcould be directed to cover only those blocks with the highest likelihoodof citations, such as the top 5%, 10%, or 20%. A performance-optimizedpatrol route within a beat for these blocks could be determined, and thepatrol route would be divided equally in intervals of time according tothe available number of parking enforcement officers 14. This approachallows each parking enforcement officer 14 to spend a similar amount oftime enforcing unique areas where citation productivity is likely to behigh. Similarly, beats could be designed based upon the assumption thatcitation output will be similar across all parking enforcement officers14 while the time to enforce a beat will vary.

Here, the generation of beats and routes through the system 20 can bedriven by feedback to the system, including disparities between thenumber of violations predicted versus the number of citations actuallyissued. For example, if a large number of citations are issued on acertain block face in a given hour, even if paid use, occupancy, andother factors suggest the citation volume should be low, that block canbe considered by the system as a block with a high probability ofviolations. Similarly, after using the method 40 design a beat tocanvass that block, if the number of violations predicted remainssignificantly higher than the number of citations issued, that variancecan be factored into future designs of beats by the system 20.

Super-Beats and Super-Teams

The traditional approach to designing and fielding beats is breakingdown. In many cities, policy dictates that when a parking enforcementofficer is returning from an assignment servicing an event, the officershould pick up any tickets encountered as they return to their own beator another assignment, even if passing through the beat of anotherofficer. This type of policy introduces several problems, as the parkingenforcement officer assigned to the passed-through beat will typicallynot be aware of the other transient officer's travel and citingactivities. When the assigned officer covers those areas of his beat,the officer will find that another officer has already cited theviolations, which can lead to complaints about “poaching,” wasted time,and difficulties in making quota or meeting performance expectations.Some cities use the rhetoric of teamwork to counter these complaints,while also recognizing the need to optimize the performance of theorganization, but lack any real time computational support for making aparadigm shift away from individual officer performance and towards trueteam effort.

In a broader sense, one of the problems with assigning parkingenforcement officers rigidly to traditional dedicated beats is that theofficers are not always deployed where they are needed most during aduty shift, as the parking enforcement officers will ordinarily stick topatrolling their own beats, except in the case of emergencies or whenresponding to unplanned events managed by dispatch. Moreover, by itself,the traditional beat approach has no principled provisions for assigningmultiple officers to two “hot spots,” that is, two different locationsin need of attention falling within the coverage of the same beat, ifthe hot spots occur at the same time. Further, the supervisors anddispatchers who respectively make the decisions on how to handle plannedevents versus emergencies and unplanned events generally make theirdecisions without any real time computational support, albeit in theabsence of a full understanding of the effects of different choices. Inshort, supervisors and dispatchers “fly blind” and rely on memories ofpast experience, gut feelings, and policies, rather than up-to-datedata. Furthermore, when dispatched parking enforcement officers respondto unplanned events, other duty activities on their beats are put onhold, which, in many circumstances, can lead to suboptimal performancefor the organization.

One alternative to traditional beats involves eliminating beatsaltogether through dynamic beats. For instance, the Beat Generatorproduct, licensed by Xerox Corporation, Norwalk, Conn., makespredictions about expected citations. The product divides expected workby the number of parking enforcement officers available and gives eachofficer an area or a route to patrol during a duty shift, but at thecost of omitting support for planned and unplanned events.

Notwithstanding, the problems attendant to traditional beats can beaddressed by creating by combining individual beats into a combinedparking enforcement region known as a super-beat. As summarized inTable, 1, the super-beats approach differs from the traditional beatapproach in several ways. Starting with the same parking enforcementofficer staffing, the super-beats approach suggests combining several,for instance, three or so, beats together into a single super-beat.Policy then mandates that the officers on the super-beat work togetheras a team to maximize their collective performance, along withincentives structured to encourage teamwork and optimization.Traditional beats, dynamic beats, such as provided by the Beat Generatorproduct, and Super-Beats are compared in Table 2.

TABLE 1 Traditional Approach Super-Beat Approach Assign one officer perAssign a team of officers to a super- regular beat. beat. No computersupport for System supports optimizing optimizing assignments.assignments. Planned special assignment from Planned special assignmentsfrom supervisor. No real-time supervisor. Real-time dashboard oversight.oversight for dispatchers and supervisors. Unplanned events assigned byUnplanned events assigned by dispatcher. No computer dispatcher. Systemhelps optimize support for optimizing. plans. When officers “pick up”tickets When officers pick up tickets on on others' beats, offersothers' beats, this is factored into A1 complain about poachingrecommendations. Credit is shared and and wasted motion. waste motion iseliminated. Individual performance Individual and team performancemeasured and incented. measured and incented. No passing of enforcementdata Enforcement information is delivered across officers or shifts. toofficers on patrol dynamically. Sub-optimal - because it does Optimalfor a team and across teams not place officers dynamically whendispatchers can change team where they are needed. assignments.

TABLE 2 Traditional Beats Dynamic Beats Super-Beats Divides labor of YesYes Yes force. Optimizes No Yes Yes enforcement assignments. Supportsplanned With supervisor, No Yes events. but without optimization.Supports With dispatcher, No Yes unplanned events. but withoutoptimization. Can be used and (Is breaking Use variable Yes adopteddown) number of incrementally. separate squads of officers for dynamicwork.

Of the three approaches, the super-beat approach is the only one thatprovides optimization and support for planned and unplanned events.Provisions for dynamic planning make the super-beats approach especiallysuited to cities where the parking enforcement organization must respondto emergencies or unplanned events. The same capabilities also enable acity to vary the size of the enforcement force during the day, asneeded. Organizations can also deploy or test the super-beats approachincrementally. Typical parking enforcement departments have years ofexperience and a culture built up around organizing their activities bybeats. Approaches, such as dynamic beat generation, offer a promise ofoptimization, but at the same time, require organizations to do awaywith familiar pre-defined beats altogether. For many departments, thatapproach is too radical of a change and that hinders adoption.Super-beats enable a parking enforcement organization to keep existingbeats for those situations in which traditional beats work well enoughand to try super-beats and teamwork in select areas where optimizationis suffering. For example, an organization can take a few squads anddeploy them over a portion of the area as super-teams on super-beats,while measuring changes in overall performance. This type of incrementaladoption offers less risk and enables an organization to try things out,test and demonstrate utility, and motivate other supervisors and squadsto transition to a super-beat approach.

Optimizing Patrol Routes

Once a beat is designed, a recommended patrol route within the beat forthe parking enforcement officer 14 assigned to that beat can beoptimized. FIGS. 5-7 are map diagrams respectively showing, by way ofexamples, an officer activity interface 180, 190, 200 presenting optimalpatrol routes within a beat for different times of the day. The numberof potential citations typically changes over each hour of a given day.Consequently, the optimal route for enforcing parking regulations alsoneeds to change throughout the day. Referring first to FIG. 5, at 9:20a.m., the recommended route covers the entire area of the beat.Referring next to FIG. 6, at 1:15 p.m., the recommended route expands asadditional parking meters become operational. Finally, referring to FIG.7, at 7:22 p.m., when there may be fewer violations or time constraints,the recommended route focuses on just those blocks with the mostcitations, with the parking enforcement officer 14 being able to opt toenforce the top 5%, 10%, 25%, or any other fraction of the beat.

A patrol route within a beat can also be optimized based uponanticipated productivity. The computation is performed by theopportunity predictor 75 components of the planner and recommender layer62 by calculating the anticipated number of parking violations perblock, with resources deployed to maximize citation issuance. Thecorrelation between the average time that a vehicle is parked and themaximum time that vehicles are allowed to park in each metered parkingspace is a key consideration in this approach. In the city ofIndianapolis, Ind., for example, 95% of the parked vehicles stay for aperiod of less than three hours. Consequently, parking enforcementofficers 14 returning to previously-visited parking spaces on a blockafter three hours will, in all likelihood, find a different vehicleparked there. Further, 82% of the vehicles remained parked for less thantwo hours, and 57% for less than one hour. Such values help to determinethe likelihood of finding infractions on a block after the block hasbeen canvassed by an officer. Further, such factors provide insightsabout when a block should again be included in the options displayed toa parking enforcement officer, along with their priority.

The various patrol route within a beat optimization approaches,including the approaches based upon time to enforce and expectedcitations, can be combined. The number of potential violations per blockmay vary significantly, and the parking enforcement officer 14 may needto cover those blocks where the probability of issuing the most ticketsis highest first. To accomplish this goal, the time to enforce can beoptimized with the blocks where productivity will be greatest beingafforded higher priorities. Thus, the parking enforcement officer 14will be provided the patrol route within a beat that leads to the mostcitations early, while trying to mitigate the time taken to enforce thebeat. Dispatcher to officer communications will next be discussed.

Conditioned, Autonomous Messaging

When an unplanned event arises, a dispatcher 13 needs to assess theevent, estimate the number of parking enforcement officers 14 that willbe required and assign the officers 14 to the event. Since dispatchers13 cannot directly see the full context of an officer's currentsituation on patrol, including unlogged engagements, dispatchers 13typically need to ask the officer about his availability. In principle,a dispatcher 13 could simply just ask the most appropriate parkingenforcement officer 14 about his availability. This approach avoidsinterrupting the other officers 14, yet the contacted officer 14 may bebusy and unable to respond. Furthermore, if the officer 14 takes toolong to respond, the dispatcher 13 could move to the next mostappropriate officer 14 and so on until an officer 14 is assigned anddispatched. However, in a worst case scenario, valuable time may bewasted searching for an available officer 14, which could be problematicto unacceptable in a situation that requires a short response time.Alternatively, to avoid the delays of sequentially contacting individualparking enforcement officers 14 until an available officer is found, adispatcher 13 could poll all of the officers en masse about theiravailability. Communicating with all officers, though, would be resultin an overwhelming stream of inquiries and interruptions to officers 14,thereby creating significantly more overhead than is usually needed tosupport dispatch coordination.

To reduce coordination communications overhead, computer systemspartnered with each officer 14 are harnessed to intermediate in thecommunications. For example, an officer's mobile computing device, suchas a tablet computer or smartphone, can regularly update its status bycommunicating with the parking enforcement support services server 21and the dispatcher's computer system would get the information from theserver 21. Thus, human interruptions are minimized.

Here, each computer system sorts, prioritizes and filters information,so that irrelevant availability requests do not overwhelm officers 14,as well as burden dispatchers 13, supervisors 12, or other personnel.Each officer's computer partner carries out conditioned, autonomousmessaging to support coordination with reduced communications overhead.Irrelevant availability requests are filtered by setting the conditionsin which actually interrupting an officer is not necessary to determineavailability. By way of example, these conditions can include:

-   -   If an officer is already working on a service activity request        at the same or higher priority as a new service activity        request, the officer is considered to be unavailable and the        response can be autonomous.    -   If an officer is on patrol and driving, the officer is        considered to be not engaged in driving to or directly handling        an event and is considered to be available.    -   If an officer is on a break and the priority of the event is        below a preset threshold, the officer is considered to be        unavailable and the response can be autonomous.    -   If an officer is on a break and the priority of the event is        above a preset threshold, the officer is considered to be        available.    -   If the officer is writing a citation, the officer is not on a        high priority assignment and is considered to be available.    -   If an officer is reviewing next enforcement activities to        perform, the officer is not on an assignment and is considered        to be available.    -   If enough suitable officers have already responded as being        available, there is no point in querying other officers.    -   If an officer is working on a lower priority activity, the        officer could be presented with a short query or sound to which        the officer could respond with a pre-arranged code word to keep        the interruption at a minimum.        Still other conditions are possible. The visualization of        citation opportunities will now be discussed.        Visualizing Citation Opportunities on Blocks

Through the computer systems partnered with each officer 14 through amobile computing device, such as a tablet computer or smartphone, thesystem 20 provides information displays about citation opportunities toguide parking enforcement officer 14 patrol route movement within abeat. The citation opportunity information displays can also be used tohelp manage the assignment of beats to officers or to recommend actionsto officers 14 in real time.

The information displays constantly present new priorities as a parkingenforcement officer 14 moves on his patrol route within his beat. FIGS.8-10 are diagrams respectively showing, by way of examples, an officeractivity interface 210, 220, 230 presenting recommendations for probableparking violations in an officer activity interface along a patrol routewithin a beat as updated based upon parking enforcement officer 14movement. The parking enforcement officer's current position is shown asa solid dot. The lines denote block faces, and the various colors,shading or line patterns align with different thresholds representingthe likelihood of issuing a citation. For instance, a solid (or darkred) line represents the blocks or block faces where the probability offinding violations is the highest, or “Highly Probable,” a dashed (ororange) line represents areas where finding violations is “Probable,” adotted (or yellow) line represents areas where finding violations is“Somewhat Likely,” and a dashed-dotted (or dark blue) line representsthe areas where finding violations is “Least Likely,” such as in thoseareas that were just canvassed by the parking enforcement officer 14.The methodology for displaying these options is not limited to thecolors or line patterns noted and can include any variety of colors orline patterns representing an indefinite number of thresholds.

Here, the parking enforcement officer 14 is walking from north to south.The recommended paths discussed with reference to FIGS. 5-7 supra showwhere the most number of potential citations can be found. Theinformation display reflects that a high probability of issuingcitations exists directly south of the parking enforcement officer'scurrent location. Those blocks most recently visited by the officer 14are less likely to offer additional violations because any illegallyparked vehicles have already been cited. City policy or ordinance mayalso prohibit the issuance of more than one violation for a particularinfraction during a given time window.

Notwithstanding the recommended paths, the parking enforcement officermay decide to follow an alternative path than the patrol route within abeat recommended. Instead of proceeding south, the parking enforcementofficer 14 turns to the right. FIGS. 11-13 are diagrams respectivelyshowing, by way of examples, an officer activity interface 240, 250, 260presenting adaptive recommendations for probable parking violationsalong a patrol route within a beat as updated when a recommendation isnot followed. Each time that the officer 14 reaches an intersection, theinformation display can suggest the direction that the officer ought tofollow to issue the most citations so as to optimize performance withrespect to time and violations. The information display dynamicallyadapts to the parking enforcement officer's current location andgenerates new recommendations for issuing the most citations.

Citation Opportunity Clusters

Patrol route within a beat recommendations can be presented in otherformats. FIG. 14 is a map diagram 270 showing, by way of example,clusters of predicted or issued parking violations or citations. Ratherthan color- or line-coding block faces on a map with probabilities,clusters of predicted or issued violations or citations can be displayedon a map. The clusters are re-computed as the officer zooms in on themap. In this way, the visualization trades off the ability to see anaccurate count of citations against the precise location of citationsfor a given amount of display real estate.

Visualizing Activities

In addition to patrol route within a beat recommendations, an activitymap of the path of travel taken by an officer through his beat can begenerated. FIG. 15 is a map diagram 280 showing, by way of example, anactivity map for a pair of parking enforcement officers while working ontheir respective patrol routes within a beat. The activity mapsummarizes the movement of each officer in time, space, and activity.Line-coded (or color-coded) paths for two parking enforcement officerson Beat 3, Adams and Baker, are depicted for the period from 8 a.m. to10 a.m. A directional marker, such as a color-coded arrowhead, leafhead, pointer, or needle, is first selected. The main travel pathcovered by each officer is then shown as a solid line that is made up ofconnected segments with an arrowhead placed somewhere on the segment. Inone embodiment, the arrowheads are placed on the end of each segment atregular times intervals; alternatively, the arrowheads can be placed inthe middle of each segment (or somewhere on each segment), such that thepassage of time is indicated by estimating that the officer 14 wastraveling in a straight line at a constant velocity in-between each pairof time-space locations, as signified by the arrowheads.

Here, the arrowheads are placed at a location on the activity map thatsignifies the officers' whereabouts at constant half-hour timeintervals, although other time intervals could be used. The arrowheadson each travel path demark both the officers' main directions of traveland boundaries of sequential half-hour time intervals. Thus, in thisexample, an arrowhead for each officer is depicted at thirty-minuteintervals. For officer Adams, the circle at Wyman Elementary Schoolmarks 8:00 a.m.; the first arrowhead shows where he is at 8:30 a.m.; thesecond arrowhead shows where he is at 9:00 a.m.; and so on. In a furtherembodiment, when several travel paths are displayed on the same activitymap, the travel path corresponding to each officer can be depicted witha modality that is unique to that officer, such as by color, texture,line width, and so forth.

In some situations, the travel paths depicted on the activity map canbecome cluttered, such as when the officer is moving so slowly throughhis beat that the arrowheads that terminate each connected segmentappear close together or start to collide. In a further embodiment, thetravel paths can be decluttered by replacing select directional markersthat appear along the officer's travel path with simple time markers,such as a hash mark or dot. The simple travel markers would generallyappear in place of directional markers where the connected segments areshort and the officer's direction of travel has not changed, that is, hehas proceeded along a straight line. The end of the straight line travelpath would still be terminated with a directional marker, but simpletime markers would replace the preceding directional markers. In a stillfurther embodiment, detecting a slow-moving officer may be of concernand those places on the officer's beat where his movement is slow mayneed to be emphasized, rather than de-emphasized. An indicator thathighlights the slow-moving officer, for instance, densely-spaced tick orhash marks, densely-packed dots, an icon, color, width, or other edgefeatures to call attention to the slow segment can be used. Still otherforms of decluttering are possible.

Locations of special activities are shown as icons (or with other typesof indicators or features, like color, width, line type, and so forth)along the path. In this example, color-coded (or pattern-coded) circlesare used to indicate service assignments. The main route taken by anofficer 14 along his beat can be implied based on the central positionof each segment and side trips or excursions from the main route can beshown as “clouds” or density maps, that is, “comet trails,” which conveya rough sense of where the officer traveled and where an ALPR recordedvehicle information. Alternatively, the locations of marked (parked andrecorded) vehicles could be shown as icons or dots, depending upon mapscale. In variations on the activity visualization, specific times canbe obtained either by small labels on the maps, or by pop-up labels whena viewer hovers a pointer over the map. In addition, selectable layerson the maps can indicate traffic, beat boundaries, enforcement types,expected parking violations, past performance, historical citation data,service requests, other officers and their activities, and other kindsof information relevant to enforcement.

Visualizing Activity Types

If a supervisor 12, dispatcher 28, or parking enforcement officer teammember wants a visual summary of an officer's recent activity, thesystem can display an activity map. FIG. 16 is map diagram showing, byway of example, an activity type map 290 for streets recently visited bya parking enforcement officer 14. Recent logged activities are depictedin a map format and can be overlaid on a street map to see the names ofthe streets that the officer 14 has traveled. The round-cornered boxesindicate specific logged activities. A box with a ‘C’ or similar labelrepresents a citation. A box with an ‘M’ or similar label represents amarked vehicle. A box with an ‘N’ or similar label represents a notethat the officer sent in from his mobile computer. Other types of boxesor shapes, symbols, and labels are possible.

Scenarios

A set of four scenarios will now be discussed to describe the use of thesystem and method to optimize operational processes of a fictitiousparking enforcement organization in Ocean City. These scenariosillustrate how organizational performance can be improved when parkingenforcement officers are organized into teams, rather than workingindividually on their own beats. The scenarios provide examples of howthe system and method can support teamwork by better facilitating visualrepresentation and integration of context and coordination. The userinterfaces described are intended to provide contextual awareness formembers of an enforcement team and efficient negotiation of work betweenparking enforcement officer team members, supervisors, dispatchers, andsystem modules. The scenarios include:

Scenario 1 presents a high-level introduction and overview thatdescribes the activities of management and a three-person team assignedto Beat 3, a super-beat, in Ocean City. This scenario is simplified toexclude unplanned events. The system and method described herein enablesthe team to achieve greater performance and agility than conventionallypossible. This scenario also introduces a map-based activityvisualization, as through the dynamic route predictor 74 and opportunitypredictor 75 components of the planner and recommender layer 62 (shownin FIG. 4).

Scenario 2 presents an officer's-eye view and shows the activities andintermediated communications for Officer Adams as he works on Beat 3during his duty shift. Adams' information needs are supported through auser interface on his mobile computing device. This scenario also showshow Adams experiences the computer-intermediated communications intendedto optimize team performance as provided through the message director71, observer 72, and conditional autonomous messaging 73 components ofthe computer partner and communications layer 63 (shown in FIG. 4).

Scenario 3 presents a supervisor's-eye view and shows the activities ofSupervisor Song in the middle of a duty shift. Supervisors 12 typicallyassign officers to beats at the beginning of a duty shift, providemotivational inputs when needed, and also review officer performanceafter a duty shift. This scenario presents a real time interface forsupervisors overseeing squad performance over parts of a duty shift asprovided through the resource allocator 76, response plan generator 77,coverage planner 78, and motivation recommender 79 components of theplanner and recommender layer 62 (shown in FIG. 4).

Scenario 4 presents a dispatcher's-eye view and shows the activities ofDispatcher Dance as she assesses ongoing events and takes supervisoryactions during the duty shift. Typically, a dispatcher 13 is concernedwith responding to unplanned real time events when parking enforcementofficers need to be pulled from their ongoing patrol duties. Adispatcher 13 may oversee several squads. Traditionally, dispatchers 13focus almost exclusively on public safety concerns and not on otheraspects of organizational performance, which can sometimes be an issuewith the needs of other personnel in the organization, particularlysupervisors 12, who have responsibility for their squad and its overallperformance. The dynamic planning and recommender parts of the systemassist her directly that is provided through the resource allocator 76,response plan generator 77, and coverage planner 78 components of theplanner and recommender layer 62 (shown in FIG. 4). The c-partnersoperating on the officers' mobile computing devices through the messagedirector 71, observer 72, and conditional autonomous messaging 73components of the computer partner and communications layer 63 (alsoshown in FIG. 4) intermediate communications to optimize performancewhile keeping the interruptions and overhead of coordination low for theteam. This scenario also illustrates further system-initiatedinteractions with the dispatcher 28 and parking enforcement officers 14and teams during the shift.

Background to Scenarios

Ocean City has 50 parking enforcement officers organized into six squadswho work on 30 beats. Three dispatchers oversee the beats. TheDepartment of Transportation in Ocean City created the “super” Beat 3 bycombining two pre-existing single-officer beats named beat A and beat B.In the following scenarios, Dispatcher Dance oversees the real timeactivities of two squads and also the activities of three officers inSquad 1 who work as a team covering Beat 3. FIG. 17 is diagram showing,by way of example, a mapping 300 of city block area name to city blockarea character for Beat 3 in Ocean City. Beat 3 is divided into twelveareas or neighborhoods, labeled A1 through B6. The mapping 300 showsnames of the areas of the beat for easy reference. The areas havedifferent characteristics. For example, areas A1 and B4 are residentialand have schools. Areas B2 and B3 are business and shopping districts.Area A6 has hotels, stores, and restaurants that attract tourism.

In the course of a day, officers carry out different parking enforcementand public safety tasks. These tasks can be of a high or low priority.For simplicity in the scenarios, each task requires either an hour or ahalf hour. FIG. 18 is diagram showing, by way of example, visualencodings 310 for the parking enforcement tasks. A black encodingrepresents high priority and a grey (or cross-hatched) encodingrepresents medium priority. A solid pattern represents a task requiringa full hour and a checkered pattern represents a task requiring ahalf-hour.

Scenario 1—Parking Enforcement on a Shift

The officer interface 47 (shown in FIG. 3) acts as a real-time partnerthat interacts with an officer 14 throughout a duty shift. Practicallyspeaking, supervisors 12 cannot monitor all of their officers 14 indetail through every minute of a duty shift. The officer interface 47helps reduce the workload of supervisors 12 by helping officers 14 makeoptimal choices throughout a duty shift. In comparison to a supervisor'sinterface, the officer interface 47 is intended to provide finer-grainedrecommendations and alerts to officers 14 than a supervisor 12reasonably could. In addition, since officers 14 have an immediateawareness of street conditions and other factors not visible tosupervisors 12, the officer interface 47 can offer a level of detailthat is useful, but not overwhelming. For instance, an activity plan canbe visually displayed to an officer 14 through the officer interface 47.When provided at an abstract level, the activity plan can refer toentire regions within the officer's beat that the system 20 recommends.Conversely, when provided at a detailed level, the activity plan canindicate exactly where the officer 14 ought to go on a city block.

This scenario provides a high level overview of the activities of asupervisor and a three-officer team assigned to Beat 3, a super-beat, inOcean City. This overview has no unplanned events or surprises; laterscenarios cover the same duty shift, but add unplanned events andsituation assessment, plus example designs of user interfaces andintermediated communications.

Date/Time Activity Tuesday, At 8:00 a.m. in the morning, all of theparking enforcement 8:00 a.m. officers in Squad 1 meet at the Departmentof Transportation building to pick up gear and to discuss theassignments and issues with Supervisor Song, who has already entered thepriorities and tasks for the officers into the system. Officers Adamsand Baker log into the system and see that they are assigned as a teamto cover Beat 3 for the entire duty shift. Officer Cooper is currentlyscheduled to help cover Beat 3 during the mid-day traffic peak from 10a.m. to 3 p.m. At 8:00 a.m., the highest priority for Beat 3 is trafficcontrol for picking up and dropping off students at schools in areas A1and B4. A secondary priority is covering congestion- causing doubleparking and various unsafe parking as people run errands and pick upcoffee on the way to work. Adams and Baker are both assigned vehiclesequipped with ALPR that enable them to read and record license platesand residential permit tags while driving by. 8:00 a.m.- Adams coverselementary school drop off in A1. 8:30 a.m. Baker covers the middleschool drop off in B4. 8:30 a.m.- The officers split the patrol of earlymorning crunch in the 9:00 a.m. business districts B2 and B3. 9:00 a.m.-On-street parking in the business districts is limited. In 10:00 a.m.residential areas, non-residents are allowed to park for no more thantwo hours a day. There have been growing complaints in the residentialareas surrounding the business areas that non-residents park for toomany hours. Non-residential vehicles can be identified by the lack of aresidential permit. There is also a request to check out a possiblyabandoned vehicle in B6 in the industrial area. Adams and Baker knowthat non-residential vehicles can be identified by the lack of aresidential permit.

FIGS. 19-22 are pairs of diagrams 320, 330, 340, 350 showing, by way ofexamples, prioritized parking enforcement tasks for the time periods ofScenario 1. Referring to FIG. 19, in the 9 a.m. hour, the high priorityone-hour residential marking task in A4 through A6, the medium priorityhalf-hour marking task in the B1-B5 corridor, and the high priorityshort abandoned vehicle task in the industrial area in B6 are shown. Theoverview shows the two officers splitting up the work. The coordinationin the 8 a.m. hour enables higher performance than traditionalassignments on smaller beats would permit because most of the prioritiesin this first hour are in the B section of Beat 3, that is, the former“Beat B.” If traditional beats were used, only one officer on Beat Bwould have been deployed during this hour. With teaming, the system,through the coverage planner 78 components of the planner andrecommender layer 62 (shown in FIG. 4) dynamically puts parkingenforcement officers 14 where they are needed.

Date/Time Agent Activity 10:00 a.m. Cooper joins the team to help covertasks during the busy middle of the day time period. Ocean City hasparking meters and parking occupancy sensors in the downtown area, butnot in its residential areas. To more accurately monitor parking inareas without the sensors, Ocean City's policy is to sweep those areasevery half hour when possible. 10:00 am-11:00 Adams One high priorityone-hour long task is a.m. Baker patrolling the business districts B2and Cooper B3. Baker has this assignment. A medium priority one-hourlong task is patrolling the residential parking in B4 to B6 to markvehicles that arrived in the last hour. Adams has this assignment.Another medium priority half-hour task is patrolling the B1-B5 corridorfor vehicles that arrived in the last hour. Cooper has this assignment.11:00 a.m.-12:00 Adams The 11:00 a.m. to 12:00 p.m. hour is p.m. Bakersimilar to the previous hour, with the Cooper difference that somenon-residential vehicles parked in the A4 to A6 area that was markedearlier may now be exceeding the two-hour limit. Patrolling this areanow has a high priority. The officers continue their assignments fromthe previous hour.

Referring next to FIG. 20, the patterns of the activities from 10 a.m.to noon are shown. Here, Cooper joins the team at 10:00 a.m. and patrolsthe B1 to B5 corridor from 10:30 a.m. to 11:00 a.m. From 11:00 a.m. tonoon, he patrols residential area A1 and returns to cover the B1 to B5corridor. Earlier, at 10:00 a.m., Adams patrols and marks a largecombined residential and tourism area. At 11:00 a.m., he patrols andpicks up over-time vehicles in the residential and tourism areas. Also,starting at 10:00 a.m., Baker patrols business areas for two hours.Overall, the pattern during the 10 a.m.-12 p.m. hours follows the policyof revisiting most of the residential parking areas near the businessdistricts at least once an hour during the period when violations aremost likely. This pattern includes patrol for the business district forcongestion problems and gives a little late morning coverage to theschool area in A1.

Staggered Breaks and Planned Events

Date/Time Agent Activity 12:00 p.m. Priorities during the earlyafternoon reflect several considerations. First, following laborpractices in Ocean City, officers are expected to take breaks during theday. They take a half-hour lunch break and a fifteen-minute break duringthe afternoon. By policy, officers are expected to stagger their majorbreaks when operating as a team, so that at least one officer is on dutyon a super-beat to respond to any accident or urgent unplanned event. Inthe business areas, the peak of congestion continues through the noonhour to 1:00 p.m. When possible, at least two visits to each area duringthe day shift are recommended, if possible. 12:00 p.m.- Adams (on Duringthis period, Adams takes a lunch 1:00 p.m. lunch break) break(designated by A^(B) in FIG. 21). Baker Baker covers the busy businessareas. Cooper Cooper patrols southern residential parking near thebusiness and industrial area. 1:00 p.m.- Adams During this period, Adamstakes over 2:00 p.m. Baker (on patrolling the business areas. lunchbreak) Baker takes over patrolling a distant Cooper northern residentialarea and takes a lunch break. Cooper continues the patrol of a northernresidential area adjacent to the business areas to mark vehicles fromnon-residents who park there when working in the business district.

Referring next to FIG. 21, the activities over the early afternoon areshown.

Date/Time Agent Activity 2:00 p.m.- Adams (on mini Adams takes a minibreak, but covers 3:00 p.m. break) distant northern residential areas.He Baker covers Wyman Elementary School again Cooper (on at 2:30 p.m.lunch break) Baker patrols southern residential areas to mark and pickup non-residential over-time parking. She covers the middle schoolagain. Cooper covers the business district lightly and takes a lunchbreak. 3:00 p.m.- Adams Adams does a last patrol of the northern 4:00p.m. Baker (on mini residential areas, tourism area and the break)industrial area. Baker patrols the southern residential corridor andtakes a mini break. Cooper moves to an assignment with another team onanother beat.

Referring next to FIG. 25, the activities over the mid-afternoon areshown.

Date/Time Agent Activity 4:00 p.m. During the 4 p.m. hour of the shift,the priority is to reduce congestion during rush hour. In Beat 3, themain areas of congestion are the business areas, the light industrialarea, and the tourist area. Officers are expected to patrol and focuswherever they are needed in the area. 4:00 p.m.-5:00 Able Adams takestourism area A6 and p.m. Baker business area B3. Baker takes businessarea B2 and industrial area B6.

FIG. 23 is a set of diagrams 360 showing, by way of examples, anoverview of the enforcement activities for Beat 3. Superscript,uppercase ‘B’ indicates a lunch break, and superscript lowercase ‘b’indicates a mini (15-minute) break. From this perspective, the followingobservations are shown:

-   -   Assignments generally follow the expected density of traffic in        the business, tourism, and light industrial areas.    -   Parking enforcement officer assignments in patrolling        residential areas are frequent enough to monitor non-residential        over-time parking.    -   School traffic monitoring is covered during the appropriate        hours. Here, School traffic monitoring is most needed at the        beginning of the school day at 8:15 a.m. and also at 2:30 p.m.    -   Officers have scheduled breaks and do not overlap lunch breaks.    -   Additional staff is scheduled during the peak part of the duty        shift.

Scenario 1 focuses on the rhythm of enforcement. In later scenarios, thepolicies guiding this rhythm are considered and represented and can beused to guide officer allocation levels and assignments. The laterscenarios also shows how the assignment advice that follows suchguidelines can be computed through the dynamic route predictor 74,opportunity predictor 75 components of the planner and recommender layer62, and how the interactions with officers for guidance and input can becarried out through the mobile computing device interfaces of the userinteraction layer 527 and the computer partner and communications layer63.

In the next scenarios, the scenarios cover the same duty shift, exceptthat unplanned events are added that require dynamic re-planning.

Scenario 2—Supporting Officers

Scenario 2 retells the story of the shift described in Scenario 1 fromthe perspective of Office Adams and includes unplanned events thatchange assignments during the shift. The scenario also presents mockupsof user interfaces for the system 20 that are provided by the officeractivity interface 69 and the situation assessment interface 70components of the user interaction layer 61 (shown in FIG. 4) as part ofthe officer interface 47 (shown in FIG. 3) and interactions with thesystem that are provided by the message director 71, observer 72, andconditional autonomous messaging 73 components of the computer partnerand communications layer 63 (also shown in FIG. 4). Through the officerinterface 47, officers 14 receive recommendations and alerts in supportof both revenue-producing enforcement activities and publicsafety-promoting service activities.

The officer interface 47 provides data needed by officers 14 duringtheir duty shifts. Besides computed expectations of citations, theofficer interface 47 can provide situational awareness to officers 14 byshowing activities of other officers 14 that can impact their planningor upcoming activities. The officer interface 47 can also remindofficers 14 to take breaks as needed and meet coverage requirements.Behind the scenes, computations are performed to plan optimal patrolroutes within a beat and opportunities for issuing citationsrespectively by the dynamic route predictor 74 and opportunity predictor75 components of the planner and recommender layer 62 (also shown inFIG. 4).

While on patrol, each parking enforcement officer 14 is equipped with amobile computing device, such as a tablet computer or smartphone, uponwhich a real time coordination application executes. Alternatively, thereal time coordination application could be provided on the mobilecomputing device as a Web-based program that runs in a Web browser orsimilar application. FIG. 24 is a diagram showing, by way of example, alogon screen 370 of the real time coordination application, which wouldbe used by an officer at the start of each duty shift. To logon, theparking enforcement officer 14 enters a username and password, althoughother forms of identification, authorization and authentication arepossible.

Date/Time Agent Activity Tuesday, Adams At 7:45 a.m., Officer Adams,Badge #79, 7:45 a.m. heads to the Department of Transportation buildingto pick up his gear and get any assignments and updates from SupervisorSong. Adams Adams collects his mobile computing device, vehicle keys andso on. He goes to the briefing room. After exchanging greetings withother officers waiting for the meeting to start, he activates his mobilecomputing device, a tablet computer. He logs in and goes to the homescreen to get a quick overview of his shift assignment and find histeammates. The home screen shows that his scheduled teammates areOfficers Baker and Cooper. Cooper is scheduled to be on the beat from 10a.m. to 3 p.m. Adams is assigned to Beat 3. 7:50 a.m. Adams Adams looksaround the room and waves to Baker. Cooper is not in the room yet. 7:55a.m. Adams The home screen shows that he has a message. 7:56 a.m. AdamsClicking on the message icon, he finds a greeting from Baker.

FIG. 25 is a diagram showing, by way of example, the home screen 380 ofthe real time coordination application of FIG. 24. Adams has alreadylogged in. The ‘1’ in the circle by the message icon indicates that hehas a new message. Key performance indicators in the top row of the homescreen 380 reflect Adams' status at the beginning of the shift. In thisscenario, a typical officer makes 40 citations on this beat during thisshift and does one service request. The number of expected breaks, onelunch break and two short breaks, along with the number of breaksalready taken, are also shown. Other types of performance indicators andinformational notices are possible.

FIG. 26 is a diagram showing, by way of example, a messaging interfacescreen 390 of the real time coordination application of FIG. 24. Themessaging interface screen 390 provides chat streams with the otherparking enforcement officers on Officer Adams' team and people on otherbeats and also to provide oversight management. Other forms of messagingand parties with whom messaging can occur are possible.

Shift Planning for Three Officers on a Beat

Date/Time Activity 8:01 a.m. At the meeting, Supervisor Song has somerequests for the squad. There were complaints about inadequate trafficcontrol at school drop off. He asks officers with these assignments totake status notes with pictures, so that the department can document andaddress any issues at the schools. 8:02 a.m. After the briefing meeting,Adams heads out to his vehicle, which is a standard traffic enforcementcar equipped with ALPR. Adams navigates to the home screen of the realtime coordination application and clicks on the “Plan My Shift”interface to see an Activity Map of his plan for the day. He also seesthe estimated plans for the other members of his team.

FIG. 27 is a diagram showing, by way of example, an activity mapinterface screen 400 of the real time coordination application of FIG.24. The activity map interface screen 400 is part of the “Plan My Shift”interface that shows the course of activities for the three officersduring their shift on Beat 3. The coordination in the 8 a.m. hourenables higher performance than traditional assignments on smaller beatsbecause most of the priorities during some of the hours are in the Bsection of Beat 3, that is, the former “Beat B.” If the parkingenforcement organization had used its traditional approach on the oldbeats, only one officer would have been deployed on the former Beat Bduring this hour.

The activity map interface screen 400 has time labels on the arrows,which makes seeing where all three officers are expected to be locatedat a particular time easier. Using the activity map, Adams can explorethe initial plans for the day for himself and for other members of histeam. By zooming in and out of the activity map, he can get moredetailed information about different parts of the area. He can alsooverlay information on the routes of the other officers on his team, aswell as contextual information, such as traffic patterns or citationhotspots.

FIG. 28 is a diagram showing, by way of example, an alternate activitymap interface screen 400 of the real time coordination application ofFIG. 24. The activity map interface screen 400 provides an officerreal-time dashboard with velocity and arrows that indicate the routestaken by the officer 14 during an adjustable time period. The day of theweek can be selected to allow review of the officer's activities.

In addition, levels of detail, such as streets, fine, hot areas,officers, and numbers and locations of parking citations, can beselectively included in the activity map interface screen 400 bydesignating user-selectable indicators, such as icons, that are added tothe activity map. Here, squares mark the actual positions of the parkingcitations that the officer 14 has found. The travel path depicted in theactivity map is constructed, so that the travel path goes through thepositions of the parking citations in chronological order. (Otherorderings of the parking citations are possible, such as by numbers ofparking citations, although such an ordering would not correspond to theofficer's travel path over time.) By clicking on a square, the user canlearn more about that individual citation. In a further embodiment, thesquares can be omitted, so that only the travel path of connectedsegments and arrowheads is shown, or the travel path can be omitted, sothat only the positions of the parking citations are shown. In a stillfurther embodiment, the parking citations are displayed on the activitymap together with a slider control (or similar map zooming control) forselecting a time of day. As the user moves the slider control, the size(or other property) of those parking citation indicators that arenearest to the selected time of day are highlighted, increased, oremphasized, while dimming or decreasing the size or saliency of theother indicators.

FIG. 29 is a diagram showing, by way of example, an alternative activitymap interface screen 420 of the real time coordination application ofFIG. 24. The alternative activity map shows where and when activitiestake place and uses animation. Thus, when a viewer pushes the playbutton, the officer icons move in a time-synchronized fashion over theofficers' patrol routes within their beats. In a further embodiment (notshown), the officers' movements are animated with a “comet trail”following each officer's icon in a format similar to the activity mapinterface screen 420.

One benefit of having a static visualization versus an animatedspace-and-time visualization is that the parking enforcement officerscan get a sense of coverage without having to remember each other'sstreet-by-street movements throughout the day. Activities aredynamically updated by the system to reflect tasks assigned bysupervisors and dispatchers. Usually, a coarse view of the shift isprovided initially. When on patrol, the officers can request detailedturn-by-turn directions, although many officers may not need or wantthat level of advice.

Shift Planning for Three Officers on a Beat

Event Capture—Documenting a Service Assignment

Date/Time Activity 8:05 a.m. Adams drives to his first serviceassignment, which is traffic control for student drop off at WymanElementary School. 8:10 a.m. He carries out traffic control at theschool. 8:20 a.m. Remembering Supervisor Song's request, Adams documentsthe traffic situation at Wyman. He takes a picture of some potential buscrowding and notes that the volunteer parents at the school for trafficcontrol seem well organized. He captures the picture in a tagged note inthe system.

FIG. 30 is a diagram showing, by way of example, a notetaking interfacescreen 430 of the real time coordination application of FIG. 24. A notetaken by Adams, documenting traffic and the situation at WymanElementary School, is filed in the system as part of documenting thesafety situation in the morning at schools.

Dynamic Replanning—Due to Delay in Completing an Activity

Date/Time Activity 8:30 a.m. Adams returns to his vehicle. From hisearlier viewing of the “Plan My Shift” interface, he remembers thatafter the school drop off, the dynamic route predictor 74 component ofthe planner and recommender layer 62 had suggested that he patrol theresidential neighborhood A1 and the business neighborhood B2. Since hewas unexpectedly delayed at the school, there is not much time to doboth activities. Adams goes back to the “Plan My Shift” interface, whichhas been dynamically updated. The system recommends a route to B2 andshows an estimate of the likely number of citations that he might findalong the way on Park Avenue West and on 16th Avenue in the businessdistrict. Adams interprets the map as meaning that he will likely pickup a citation on the way to B2, and that he is likely to pick up morecitations once he starts patrolling the business district. 8:31 a.m.Adams starts driving on the recommended route to B2. 8:40 a.m. Drivingalong Park Avenue, Adams sees a car parked next to a fire hydrant. Heissues a citation and continues on to 16^(th) Avenue. 8:45 a.m. As hearrives on 16^(th) Avenue, Adams uses the Orienting “Plan for thisNeighborhood” interface screen of the map interface zoomed to a blockview to get a detailed sense of how to best enforce parking regulationsin the neighborhood.

FIG. 31 is a diagram showing, by way of example, an orienting interfacescreen 440 of the real time coordination application of FIG. 24 showinga recommended route for parking enforcement. The orienting “Plan forthis Neighborhood” interface screen 430 displays a preferred route forAdams to leave the school assignment and proceed to the businessdistrict B2 for enforcement. The system also roughly predicts the numberof citations that Adams may find during this trip. The message box atthe bottom of the screen tells Adams that he can click on a neighborhoodfor a more detailed orientation. In one variation of the activity mapdesign, Adams could just zoom in without switching to an “Orienting”view mode and the system would provide the zoomed-in view labeled withinformation that the system predicts he will need to carry out his nextexpected activity of patrolling the neighborhood.

Understanding Context and Enabling Context Alerts

Date/Time Activity 8:45 a.m. Adams decides to sweep through the streetsin B2 that the opportunity predictor 75 component of the planner andrecommender layer 62 predicted would be most productive. He sees thatthe expected number of citations is relatively high for this area andtime period. He clicks on the proximity alert button to request audioalerts to assist him with looking for parking regulation violations ashe drives near where other officers have found violations in the recentpast. 9:00 a.m. A common problem in residential neighborhoods,especially in areas adjacent to a business district, is thatnon-resident workers park in the residential district. However, if theyend up parking for longer than the time allowed for non- residents, theyrisk getting cited. For example, the regulations for the neighborhood inthis scenario limit non- resident parking to two hours. Holders of validresidential parking permits can park all day. Adams moves from B2 andbegins patrolling the residential and tourism areas A4 through A6. A5 isone of the areas where there have been problems with non-residentsparking in the neighborhood. 9:05 a.m. Adams uses the opportunitypredictor 75 component again, which advises him to turn on the ALPR unitand start in Area A5. The ALPR will electronically record where and whenvehicles are parked and also check the validity of their residentialpermit permits, if applicable. 9.15 a.m. Adams patrols Area A5. Audioalerts from his mobile computing device tell him to look down alleys andcheck bus zones as he nears them, as he has sometimes overlooked alleysand bus zones in the past. He has the ALPR activated, so the system isrecording the license plate numbers and residential permits of vehicleson the streets as he drives past. Adams spots three parking regulationviolations and issues citations.

FIG. 32 is a diagram showing, by way of example, an orienting interfacescreen 450 of the real time coordination application of FIG. 24 focusingon a business area of the beat. The orienting “Plan for thisNeighborhood” interface screen 450 focusing on business area B2. Thisinterface guides a parking enforcement officer to where to look forparking regulation violations by showing where previous citations haveusually been found, as provided through the opportunity predictor 75component of the planner and recommender layer 62. Citation countsappear in circles on the screen and refer to the number of citationsgiven during this hour during a recent time period, such as during thepast two weeks. Alternatively, the circled citation counts could benormalized to give expectations over the next hour.

FIG. 33 is a diagram showing, by way of example, an orienting interfacescreen 460 of the real time coordination application of FIG. 24 showingoptions for audio global positioning system (GPS) alerts. The parkingenforcement officer can turn on audio alerts for selected categories ofparking regulation violations, so that an audio alert is generatedduring patrol when the officer approaches vehicles that match theselected categories of violations.

Alerts and Response to Unplanned Event

While Adams is on patrol, a service request for a tag-and-tow operationcomes in to the dispatcher. The dispatcher sends the service request toAdams and, when he accepts the service request, Adams' plan for hisshift is modified and routing suggestions are offered to him.

Date/Time Activity 9:45 a.m. Using her situation monitoring interface,Dispatcher Dance notices a new incident. The information about theincident comes from a 9-1-1 emergency call from one Nancy Wong at 176Lafayette Street in area A5. The caller says that there is a truckblocking her driveway. Ms. Wong needs to drive to a doctor's appointmentand cannot find the truck's driver. 9:46 a.m. Dance opens the interfaceto the resource allocator 76 component of the planner and recommenderlayer 62, which suggests that Adams gets rerouted to respond to thisincident, as he is the closest parking enforcement officer to Ms. Wong'slocation. Dance puts the plan in motion by pressing the “Go” button,which triggers the system to send a request to Adams to respond to thecall. 9:47 a.m. Adams is almost at the end of a patrol sweep in A5. Theservice request appears on his screen together with an updatedrecommended route and estimated time of arrival. 9:48 a.m. Adams acceptsthe request. He is almost finished with his sweep of A5 and heads to 176Lafayette Street. 9:55 a.m. Adams arrives at 176 Lafayette Street. Thedriver of the truck still cannot be found and the truck is clearlyblocking the driveway. Adams calls the towing service. He issues acitation for the truck and takes a picture of the situation. He sendsfeedback to the system to document the incident. Policy requires that hestay on the scene until the tow truck arrives and takes the illegallyparked truck away. 10:10 a.m. After the tow truck arrives and startstowing the truck away, Adams brings up the Orienting “Plan for thisNeighborhood” interface for advice on what to do next.

FIG. 34 is a diagram showing, by way of example, a patrolling interfacescreen 470 of the real time coordination application of FIG. 24. Theinterface shows the service request from the dispatcher regardinghandling a call for a tag-and-tow for a truck that is blocking adriveway. The screen gives Adams an opportunity to accept or decline theservice request and also shows a recommended route and estimated traveltime to get to the location.

Context Awareness, Activity Tracking and Plan Adjustments

Dispatcher Dance relies on computations performed by the resourceallocator 76 and the response plan generator 77 components of theplanner and recommender layer 62, which is further discussed infra withreference to Scenario 3. The resource allocator 76 is responsible foridentifying the resources needed to handle unplanned events. Theresponse plan generator 77 is responsible for creating the operationalplans for the teams and recommending adjustments to the operationalplans throughout the day as the situations change. The blocked drivewayand subsequent tag-and-tow activity, for instance, is an unplanned eventand perturbation from Adams' original plan for his shift. Scenario 4,infra, describes how Dispatcher Dance uses her situation assessment andmonitoring interface to interact with the resource allocator 76 and theresponse plan generator 77. In the example scenarios, the resulting newplan is not much different from the old plan and does not require anycoordination with the other team members.

Here, while Adams is working in a residential area, Officer Edwardspasses through his area and picks up some citations along the way.Adams' activity map gives him contextual awareness of the otherofficer's activities, coverage, and citations. FIG. 35 is a diagramshowing, by way of example, an orienting interface screen 480 of thereal time coordination application of FIG. 24 showing a recommendedroute for parking enforcement in a portion of the beat. Here, theorienting “Plan for this Neighborhood” interface screen 350 shows aportion of the A2-A3-A5-A6 region recommended to Adams, along with arecommendation that Adams sweep every half-hour in the 10 a.m. to 11a.m. hour to increase compliance on regulations for non-residentialparking. The screen also shows citation counts for this hour from recentweeks. Since Adams recently patrolled 18^(th) Avenue while travelling tothe tag-and-tow service request, the system recommends that he begin bydriving along 17^(th) Avenue to the mixed residential and tourism areain A6, which has the highest number of likely citations. Thecross-hatched (darker green shaded) area of the screen shows where Adamshas already patrolled.

In general, the activity maps for Adams and other officers arecontinuously updated, and, in particular, Adams' priorities now need tobe changed to account for Officer Edwards' ticketing activities.Information about Edwards' ticketing activities is added to the ActiveRepresentational Model in the system's data base, which tracks thecurrent status of the city. The information added to the ActiveRepresentational Model includes both the citations that Edwards pickedup, the information that he has patrolled part of Adams' area, and anylicense plate and registration information that was picked up by theALPR on Edwards' vehicle. The opportunity predictor 353 components ofthe planner and recommender layer 62 uses this newly-added informationto determine that Adams no longer needs to travel to that area in lightof Edwards' having already done so. The opportunity predictor 353 nowmodifies its recommendations for Adams' activities.

Date/Time Activity 10:11 a.m. Since Adams recently patrolled 18^(th)Avenue when he drove to the tag-and-tow service request, the dynamicroute predictor 74 component of the planner and recommender layer 62recommends that he drive along 17^(th) Avenue to the highest densitycitation area in A6, which is mixed residential and tourism. Adams headsoff to A6 with the ALPR activated and begins patrolling the shadedregion on a route that he picked for himself. As in the earlier part ofthe scenario, Adams could set audio alerts to remind him when he is nearplaces where he typically overlooks citations. Adams picks up ticketsmainly in the predicted areas. Parking in A2 and A3 is pretty sparse.There was only one ticket found there in the last hour and no newnon-residents have parked there since. 10:50 a.m. Officer Edwards, on adifferent team, is returning from a traffic control assignment. Hisroute takes him through part of Adams' area, the eastern portion of A3and A6. While Edwards passes through, his ALPR marks vehicles in theregion and he also cites one non-resident vehicle that has passed thetwo-hour limit. He gives a citation to a car parked too close to a firehydrant. As Edwards enters and works in Adams' area, Adams' patrollinginterface alerts him to the presence of Edwards.

FIG. 36 is a diagram showing, by way of example, an activity mapinterface screen 490 of the real time coordination application of FIG.24 showing a portion of the beat. The screen shows a portion of theA2-A3-A5-A6 region patrolled by Adams and also shows the path taken byEdwards passing through the region with the citation that he issued.

Situation Assessment and Lunch Alert

Date/Time Activity 12:00 p.m. As Adams approaches the noon hour, hereviews his work and is prompted by the system to take a lunch break.Adams accepts the lunch recommendation and takes lunch near a coffeeshop. 12:45 p.m. Adams marks his break as complete and resumespatrolling in A6, picking up several citations. 1:05 p.m. At 1 p.m., thesystem interrupts his patrolling display to alert Adams about arecommended change of focus. The optimized area for him is now A6-B2-B3.1:10 p.m. Adams begins patrolling B3.

FIG. 37 is a diagram showing, by way of example, a shift reviewinterface screen 500 of the real time coordination application of FIG.24. The screen, as seen by Officer Adams at noon, shows his statisticsso far and where he spent his time in Beat 3. The screen recommends thathe take a lunch break.

Unplanned Event and Rebalancing the Assignments

FIG. 38 is a diagram showing, by way of example, a patrolling interfacescreen 510 of the real time coordination application of FIG. 24. In thisscenario, after 1 p.m., the patrolling interface screen 380 changes forAdams to advise him to switch his attention to B1, B2, and B3. Thedensity of expected citations suggests that his attention should bemainly on B3, with decreasing attention on B2 and B1. After Adamsswitches to his new assignment in the 1 p.m. to 2 p.m. hour, an accidentoccurs in area A1. (This account of an accident as an unplanned eventdiffers from the story in Scenario 1.)

With support of the coverage planner 78 components of the planner andrecommender layer 62, the dispatcher changes the assignments to the teamto handle the accident and rebalance the remaining activities among theother parking enforcement officers on the team. Scenario 3, infra,discusses how Dispatcher Dance uses the coverage planner 78 through thesituation assessment interface to review the situation and makerecommendations. FIG. 39 is a set of diagrams showing, by way ofexamples, changes 520 in duty shift task assignments for Adams, Bakerand Cooper. Baker is on break and does not change her assignment. Cooperleaves his assignment on A5 and A6 to do traffic control for theaccident in area A1. Cooper is expected to need 30-40 minutes to coverthe accident. Adams picks up the high priority part of Cooper's previousassignment in A6 and drops his own previous lower priority assignment inB1.

Date/Time Agent Activity 1:20 p.m. Dance Dance receives a call about atraffic Baker accident in area A1. Baker and Cooper Cooper are closestto the accident. Baker is on a lunch break. Dance offers the assignmentto Cooper, who accepts the traffic control assignment for the accidentand drives from A5 to A1. 1:25 p.m. Adams The patrolling interfacescreen shows Adams his new recommended focus area and integratesinformation about where Adams or Cooper recently patrolled B3 or A6, sothat Adams will know where coverage is needed. The screen alsorecommends a starting route for Adams.

FIGS. 40-41 are sets of diagrams 530, 540 showing, by way of examples,planned team assignments respectively before and after an accident in aregion of the beat. Re-planning also takes place for the 2 p.m. hour

FIG. 42 is a diagram showing, by way of example, a patrolling interfacescreen 550 of the real time coordination application of FIG. 24 showingthe re-assignment of duty shift tasks to cover the accident. The screenalerts Adams about Cooper's re-assignment to cover the accident in areaA1 and recommends that Adams focus his attention on B3 and A6. Theshaded (light green) area shows the focus areas and the cross-hashed(dark green) area shows the parts of the assignment recently covered byeither Cooper or Adams.

Date/Time Agent Activity 2:00 p.m. Cooper By 2 p.m. Cooper has clearedthe accident in A1. 2:00 p.m. Dance The coverage planner 78 component ofthe Cooper planner and recommender layer 62 Adams reconsiders thelocations of the parking enforcement officers for the 2 p.m. hour. Giventhe changed locations of the officers since the original plan was made,the coverage planner 78 component determines that some driving time canbe eliminated by re-planning the 2 p.m. hour and recommends that Cooperand Adams switch assignments in the 2 p.m. hour. 3:00 p.m. Adams As inprevious assignment transitions, Adams uses his patrolling interfacescreen to get recommendations for his work in the 3 p.m. hour.

FIG. 43 is a set of diagrams 560 showing, by way of examples, plannedteam assignments for an afternoon. Adams and Baker agree to trade aportion of their beats in the 4 p.m. hour.

Overriding Recommendation and Swapping Assignments

As the 4 p.m. hour approaches, Baker remembers that she wanted to run anerrand in area B2. Normally, she would have run her errand earlierduring the 3 p.m. hour while on her break, but she has not yet taken herbreak. She suggests to Adams that they trade areas in the 4 p.m. hour.Baker will cover B2 for Adams and Adams will cover B3 for Baker.

Date/Time Activity 3:45 p.m. At 3:45 p.m., Baker realizes that she didnot take her break during the 3 p.m. hour. She has a personal errand torun in area B2. Baker messages Adams that she would like to tradecoverage areas during the 4 p.m. hour. She proposes to cover B2 forAdams and Adams would cover B3 for her. 3:50 p.m. Adams agrees to theswap. 4:00 p.m. Adams finishes his sweep in area A6 and drives to B3.The observer 72 component of the computer partner and communicationslayer 63 (shown in FIG. 4) notices that Adams is in B3 and reminds him,through the officer activity interface 69 of the user interaction layer61 (also shown in FIG. 4) that his focus areas are A6-B2. Adams respondsthat he is overriding the recommendation and trading assignments withBaker.

FIG. 44 is a diagram showing, by way of example, a messaging interfacescreen 570 of the real time coordination application of FIG. 24. Thescreen shows Baker's request to trade areas with Adams at 4 p.m. and hisagreement to the trade.

FIG. 45 is a diagram showing, by way of example, a patrolling interfacescreen 580 of the real time coordination application of FIG. 24. Whenthe observer 72 component of the computer partner and communicationslayer 63 (shown in FIG. 4) notices that Adams is in B3 rather than B2,Adams is sent an alert. In this scenario, the observer module noticesfrom the GPS on Adams' mobile computing device that he is outside thearea that was recommended to him and sends him an alert. Adams canrespond that he is trading areas with Baker.

There are several ways that the change of plans could be communicatedbetween Adams, Baker and the system. Here, the system initiatescommunications after noticing that Adams is working B3, rather than B2,and that Baker is working B2 rather than B3. The system then asks theofficers to confirm that they had traded areas. Another approach forcommunicating a change by Adams involves sending a tagged message to thesystem, such as described in commonly-assigned U.S. Pat. No. 10,013,459,issued Jul. 3, 2018, and U.S. Pat. No. 10,025,829, issued Jul. 17, 2018,the disclosures of which are incorporated by reference. An event tag,which is appended to the message automatically by the mobile device, bythe officer, or by both, would cause the message to be routed to themessage director 71 component of the computer partner and communicationslayer 63 (shown in FIG. 4). Based on the event tag, the message director71 component would then be processed by an episodic analytic system (notshown) that can produce an association between organizational activitydata, here, what areas are being patrolled by which officers, andspecific events, here, Adams and Baker trading areas. The variousassociations formed by the episodic analytic system can also be used togenerate analytics about activities to evaluate organizationalperformance. In a still further variation, an officer could interactwith the map interface screen on his mobile computing device to tell thesystem which area he plans to cover, thereby overriding arecommendation.

Scenario 3—Supporting Supervisors

Supervisors 12 oversee and manage their squads of parking enforcementofficers 14 and are responsible for their squad's performance.Operationally, Supervisors 12 have responsibilities that include:

-   -   Pre-plan beat assignments for officers based upon seniority and        beat rotation.    -   Train officers on beats and rotate the officers to develop        familiarity.    -   Periodically assess officer performance, including previewing        expectations with officers prior to duty shifts, reviewing        expectations and performance after duty shifts, and presenting        expectations to officers during duty shifts.    -   Suggest changes to policies and encourage changes in activities        when appropriate.        In some parking enforcement organizations, the responsibilities        for supervisors 12 and dispatchers 13 may partially overlap.

Monitoring Officer Performance

Supervisors 12 need to know when their officers 14 are not performing aswell as they could. Here, the system 20 provides tools for monitoringofficer activities. Officers' actual performance is compared to expectedperformance based upon information fusion, planning, and computedperformance expectations.

Officer supervision is difficult and requires asking if an officer 14followed recommendations and whether the officer's performance was asgood as expectations for the recommendations. Finding answers to thesequestions is challenging and intuitively-derived answers gleaned bymentally fusing information, forming alternative plans and computingexpectations are unreliable at best. Specifically, fusing informationabout a situation is complex and requires sensors to collect datacombined with the fusion of current data, historic data, and theestimation of the current situation. Similarly, planning is a complexoptimization problem that requires finding the best order and bestroutes to work the areas of parking enforcement responsibility. Finally,computing expectations is complex because officer expectations are basedupon many factors, such as characteristics pertaining to the beat,including:

-   -   Nature of the officer's beat.    -   Time of day.    -   Day of week.        and factors specific to the officer, including:    -   Experience.    -   Familiarity with the beat.        Still other considerations and factors may influence fusing        information, forming alternative plans and computing        expectations.

This scenario considers two time segments during a duty shift duringwhich a supervisor 12 monitors and advises officer performance in realtime.

Date/Time Activity 10:11 a.m. Since Adams recently patrolled 18^(th)Avenue when driving to the tag-and-tow assignment, the dynamic routepredictor 74 component of the planner and recommender layer 62 (shown inFIG. 4) recommends that he drive along 17^(th) Avenue to the highestdensity citation area in A6, which is mixed residential and tourism.Adams heads off to A6 with the ALPR activated and begins patrolling theshaded region on a route that he picked for himself. As in the earlierpart of the scenario, Adams could set audio alerts to remind him when heis near places where he typically overlooks citations. Adams picks uptickets mainly in the predicted areas. Parking in A2 and A3 is prettysparse. There was only one ticket found there in the last hour and nonew non-residents have parked there since. 10:50 a.m. Edwards, who is ona different team than Adams, is returning from a traffic controlassignment. His route takes him through an eastern portion of A3 and A6.While he passes through, his ALPR marks vehicles in the region. He alsocites one non-resident vehicle that has passed the two-hour limit andgives a citation for a car parked too close to a fire hydrant. AsEdwards enters and works in Adams' area, Adams' patrolling interfacealerts him to the presence of Edwards. 10:55 a.m. Supervisor Songdecides to check on Adams. He brings up a situation assessment display.

FIG. 46 is a diagram showing, by way of example, a situation assessmentinterface 590 of a real time monitoring application for use by a parkingenforcement officer supervisor for execution on a personal computer.Here, the screen shows Supervisor Song checking on the activities ofAdams. The screen displays combines real time data about Adams'activities with recommendations about where citations may be found. Thescreen also shows a stream of communications to and from Adams toprovide context. Song can communicate directly with Adams from thesituation assessment interface 590.

In this implementation of the situation assessment interface 590, thetop box on the left is an officer timeline that shows activity or dutystatus codes, gaps in activity, and citations. Other performance reviewfeatures are possible, such as statistics on performance of two-partactivities, that is, those activities that require more than one pass tocomplete a citation. For example, in a time-limited parking zone, theissuing of an over-time ticket takes two passes, where a vehicle isobserved on the first pass and ticketed on the second pass if thevehicle is later observed in the parking zone past the allowable time.In a second example, a citation for an abandoned vehicle takes twopasses. Commonly, an officer will first put a warning on a vehiclesuspected of being abandoned and a citation will be issued if thevehicle is still there after 72 hours. The map interface and otherinterface screens for parking enforcement officers 14 and for oversightinterface screens for supervisors 12, that is, the situation assessmentinterface 590, can annotate “ripening” two-part activity opportunitiesfor enforcement when the allowed time has elapsed.

Although this example shows a supervisor 12 monitoring an individualparking enforcement officer 14, the situation assessment interface 590could be focused on a geographical area of inquiry or to a selectedgroup of parking enforcement officers 14, such as those officers who areon a particular squad, multiple squads, or a super-team. In addition,the situation assessment interface 590 could be used to detect low orhigh performance in real time and the supervisor 12 could then sendsuggestions or motivational messages to the officer or group inquestion. Finally, the system can compute various analytics that can beadded as performance indicators to the situation assessment interface590.

Highlighting Anomalies in Parking Enforcement Operations

Performance analytics are quantitative and qualitative measures thatconvey levels and quality of parking enforcement organizationalperformance. For example, a parking enforcement organization mightmeasure how often areas are patrolled, the average amount of time takento respond to a request, or expected numbers of citations or revenuesfrom ticket fines. Performance analytics and indicators that can be usedin reviewing parking enforcement officer or team performance will now bediscussed.

Detecting Low and High Performance in Real Time

A dispatcher 13, a supervisor 12, or other member of a parkingenforcement organization may wish to be notified if an individualparking enforcement officer 14 or parking enforcement team areperforming worse than expected. This situation can occur, for example,if an officer has fallen asleep, taken a break when they should be onduty, spent more than 30 minutes on a service task, and so forth.Performance indicators include:

Lack-of-Activity Indicators. Low performance can be detected by lookingfor a lack of appreciable activity by an individual parking enforcementofficer 14 or team. For example, low activity on the part of anindividual parking enforcement officer 14 can be detected by looking forone or more of:

-   -   Too much time has lapsed since a citation was written.    -   Too much time has lapsed since duty status changed.    -   Too much time has lapsed since a vehicle was marked.    -   Too much time has lapsed since a citation of a given category        was issued.    -   ALPR is deactivated.    -   Too much time has lapsed since the officer's mobile computing        device has moved.    -   Too much time has lapsed since the officer submitted a note.    -   Given the type of service task, too much time has been spent on        the task.    -   Any other key performance indicators are missing or low.        At a zone level, low activity can be detected by looking for one        or more of:    -   Too few citations.    -   Too few duty status changes.    -   Too few vehicles marked.    -   Too few citations of a given category were issued.    -   Officer handhelds have visited too few streets or zones.    -   Too few notes have been submitted overall.    -   Given the type of service task, one or more team members        spending too much time on the task.

Once a performance anomaly has detected, a supervisor 12, dispatcher 13,parking enforcement officer 14, or team can be notified of the detectedanomaly in a variety of ways, including:

Officer-of-Interest Map. Displaying the locations of low performingparking enforcement officers 14 on a map. In addition, when the viewerhovers the mouse over the icon in the map representing the officer 14,the system can display up-to-date information about the key performanceindicators for that officer 14, such as how they have been performingduring the most recent 30 to 120 minutes. The map may also show thelocations of all officers 14, using color, icons or other indicators todistinguish low, average, and high performers. Other performance metricsare possible.

Area-of-Interest Map. Highlighting low performing beats, super-beats,streets, or teams on a map. In addition, when the viewer hovers themouse over the area of interest, the system can display up-to-dateinformation about the key performance indicators for the officers inthat area, such as how they have been performing during the most recent30 to 120 minutes. The map may also show areas of average and highperformance, using color, icons or other indicators to distinguish thelow, average, and high performance areas. Other performance metrics arepossible.

Officer-of-Interest Table. Displaying a sorted list of low performingparking enforcement officers 14 with the lowest performers at the top ofthe list. In addition, the list can show up-to-date information aboutthe key performance indicators for the officers 14, such as how theyhave been performing during the most recent 30 to 120 minutes. The listcan also be sorted from high to low performance to highlight thoseofficers 14 with the highest performance. Other performance metrics arepossible.

Area-of-Interest Table. Displaying a sorted list of low performingbeats, super-beats, streets, or teams with the lowest performers on thetop of the list. In addition, the list can show up-to-date informationabout the key performance indicators for the officers 14 in that area,such as how they have been performing during the most recent 30 to 120minutes. The list can also be sorted from high to low performance tohighlight beats, super-beats, zones, streets, or teams with the highestperformance. Other performance metrics are possible.

Officer Comparison Methods

For the analytics discussed in the last section, average performance canbe computed in various ways to discover different kinds of anomalousbehavior, for instance, including:

-   -   Comparison to all parking enforcement officers 14 in this time        period    -   Comparison to all parking enforcement officers 14 at this time        on this day of the week    -   Comparison to performance of all parking enforcement officers 14        in all time periods    -   Comparison of this parking enforcement officer's performance to        his average performance in this time period, this time on this        day of the week, or all time periods.    -   Comparison to other parking enforcement officers 14 in the same        squad or the same organization.    -   Comparison to all parking enforcement officers 14 when working        in the current super-beat, beat, zone, or street.    -   Comparison to all parking enforcement officers 14 when working        under the current weather conditions.    -   Comparison to all parking enforcement officers 14 when working        under the current weather conditions in this time slot, day of        the week, on the same holiday, or other time period of interest.    -   Comparison to a pool of high performing parking enforcement        officers 14 or low performing parking enforcement officers 14.        Still other types of comparisons or temporal time frames are        possible.

Ways of Detecting Lack of Movement

Detecting the lack of movement of a parking enforcement officer 14 isone component of detecting lack of productive activity. Unusually (orsuspiciously) low movement can be detected in several ways, including:

-   -   Small amount of area covered.    -   Small number of streets visited within the assigned beat.    -   Total latitude and longitude change within a time range.    -   Time spent on streets uneven with too much time spent on some        streets and too little time spent on other streets.    -   Too much time spent outside of the assigned beat.    -   Too few areas covered.        Still other ways to detect low movement are possible.

Early Detection of Performance Drops

The low performance detection described in the last section will detectthe lowest performing parking enforcement officers 14 after a period oftime. However, a supervisor 12, dispatcher 13, or super-beat team membermay wish to detect such drops in performance as early as possible, sothat action can be taken to improve the situation.

Performance drops can be detected early by computing a running tally ofeach parking enforcement officer's performance, updated continuouslythroughout the day. At any moment, all parking enforcement officers 14are ranked based upon this score. To calculate each score, the systemassigns activity points to various kinds of activities. The system thencollects digital information about officer activity and tallies eachofficer's score through a process that ages the contributions of theactivity points. The activity points from an activity contribute to anofficer's score only for a period of time after the activity has beennoted; the activity points eventually cease to contribute to the scoreafter a sufficient period of time has passed. In other words, a temporalweighted average of activity points is computed, where more recentactivity points are given a higher weighting.

When performing early detection of performance drops, each activity isassigned a number of points. Activity points are assigned to eachactivity roughly based upon the amount of time required to perform theactivity and the effort or value that the activity represents. Forexample, driving down the road for one minute might be 1 point. Writingup a ticket for 2 minutes might be worth 5 points, consisting of 2minutes for the time taken and 3 more minutes for the value generated.The set of activities that are assigned points includes the activitiesdiscussed in the section supra entitled, Detecting Low and HighPerformance in Real Time. Still other types of activities can beassigned points.

Three kinds of weighting appear to be particularly effective:

Boxcar Weighted Activity—the points from each activity performed aregiven equal weight, as long as the activity was performed within thelast m minutes. After the m minutes have elapsed, the weight assigned tothe activity becomes zero and the activity no longer contribute to thescore. This function has the advantage of being easy to explain. Forexample, parking enforcement officers 14 can be told, “You get creditfor all of your activities in the last half hour,” assuming that mequals 30.

Exponentially Weighted Activity—the points from each activity performedare given a weight that depends upon the amount of time that has passedsince the activity took place. For example, an activity that was justlogged into the system gets a weight of 1. After 10 minutes, the weightis decreased to a weight of ½. After 20 minutes, the weight is decreasedto a weight of ¼. After 30 minutes, the weight is decreased to a weightof ⅛, and so forth. This function has the advantage that a drop inactivity can be detected early, but has the disadvantage of being harderto explain.

Exponential and Boxcar Weighted Activity—this function combines theBoxcar Weighted Activity and the Exponentially Weighted Activityfunctions. All weights assigned to activities performed drop to 0 aftera period of time, for instance, 60 minutes. Until then, the weights dropexponentially in the same fashion as for exponentially weightedactivity. This function may be a little easier to explain than the pureexponentially weighted activity function; for example, parkingenforcement officers 14 can be told, “You get credit for all of youractivity in the last hour, but recent activity counts more.”

Still other ways to detect drops in performance early and otherfunctions for weighting activity point scores are possible.

Advising Officer Performance

In addition, to monitoring for low performance, the dashboard interfacesof the system for supervisors 12 can provide recommendations for advicethat the supervisors 12 can give to their officers 14. In many cases,the recommendations created by the system or created by supervisors 12are similar to the examples discussed supra in Scenario 1. Such adviceprovides motivation to the officers by enhancing their sense ofautonomy, mastery and purpose, including allowing officers 14 to be ableto modify the level of detail in performance recommendations. Forexample, an officer 14 could get turn-by-turn directions to specifictargets, which reflects low autonomy and route mastery. On the otherhand, an officer 14 could choose to only receive occasional hints if theofficer is speeding by potential parking violations or ignoring ahighly-probably patch of violations, which reflects medium autonomy androute mastery. Further, an officer 14 could decide to get just abackground overview that shows nearby blocks with their predictedcitations and other enforcement opportunities, which reflects highautonomy and route mastery. Such recommendations extend the monitoringcapabilities of the dashboard from “working hard” monitoring and adviceto “working smart” monitoring and advice.

One pragmatic reason for providing recommendation capabilities tosupervisors 12 would be for deployments of the system in cities wheremobile computing devices capable of displaying such advice are not givento parking enforcement officers 14. In that situation, therecommendations can be read by supervisors 12 and passed along to theirofficers 14.

Detecting Areas where Officers are Most Needed

During a day of parking enforcement, given the number of violationsbeing observed or predicted or the number of service tasks, the parkingenforcement needs on some beats, super-beats, streets, or teams mayexceed the work that can be performed by the parking enforcementofficers 14 already assigned. This situation can be detectedalgorithmically in several ways, including:

-   -   Real time information from parking meters that have expired or        are about to expire.    -   Predictive models from historical data showing the number of        expected violations in the area, given the time of day, day of        the week, weather, holiday or non-holiday status, and other        factors.    -   The number of citations being written.    -   The rate at which streets are being visited.    -   The scheduled need for service tasks, including traffic control        for demonstrations, city council initiatives, press conferences,        scheduled signal maintenance, and so forth.    -   Unscheduled service needs, such as traffic accidents, fires,        police emergencies, signal outages, floods, snow blocking roads,        and so forth.        Still other ways to detect areas where officers are most needed        are possible.

When such areas of high need are detected, the information detectedalgorithmically can be communicated to a dispatcher 13 to call attentionto the need in a variety of ways, including:

-   -   Highlight high need areas on a map.    -   Provide a rank ordered list of the areas that need more parking        enforcement officers 14, where the areas are ranked by the        expected value of having an additional officer in that area        during the next time period.        Still other ways of notifying dispatchers 13 of high need areas        are possible.

Detecting Low Value Assignments

A parking enforcement officer 14 or team will generally be assigned to agiven beat, super-beat, street, or zone at any point in time. Such anassignment may turn out to have higher or lower value based upon anumber of factors, such as:

-   -   The number of violations in that area at that time.    -   Scheduled or unscheduled services needed in that area at that        time    -   Whether parking meters and other equipment are functioning. For        example, the parking meters may be bagged in that area to show        that they have been taken out of service or are in need of        servicing.        Other factors are possible.

Detecting that a parking enforcement officer 14 or team has beenassigned to an unproductive zone early is valuable. The situation can bedetected by looking for signs that their work during the next timeperiod will likely be of relatively low value if they continue workingin their current role and area. Likely low value work can be detected bylooking at:

-   -   The rate at which parking enforcement officers 14 in that area        typically find citations.    -   The extent to which the streets in that area have been visited        recently, by any parking enforcement officer 14.    -   Whether the any parking enforcement officers 14 are scheduled        for service duties in the next time period.    -   Whether reports that the streets are blocked, parking places        roped off, parking meters are bagged, and so forth have been        logged by the system.        Other indicators of likely low value work are possible.

The system can compute a predicted low value work score for each parkingenforcement officer 14 for the next 30 minutes, hour, or any other timeperiod. The system can make the predicted low value work score availableto a dispatcher 13 or super-beat team, as follows:

-   -   Display the information on a dynamically updated map.    -   Provide a sortable data table, in which officers 14 with low        value predictions are shown at the top.        Other ways to provide the predicted low value work score are        possible.

Reassigning Underutilized Officers

A dispatcher 13 or a super-beat team may discover that they need one ormore additional parking enforcement officers 14 to support the currentlevel of workload in their area. The need for reassignment may betriggered by an explicit event or may be simply be detected from theroutine information flow about the status of parking regulationviolations and parking enforcement officers 14. For example, a givenbeat, super-beat, zone, or street may have too few officers 14 given thecurrent number of parking violations that are being observed. Thissituation may result from an unscheduled event, such as a trafficaccident, fire, or traffic signal outage, an officer 14 who is unable towork, or from a larger-than-expected crowd of motorists and parkingregulation violations.

Alternatively, a dispatcher 29 or super-beat team may consider thepredicted low value work score described supra in the last section,identify the parking enforcement officers 14 or teams currently on theassignments having the lowest values, and attempt to find them highervalue assignments. A reassignment will generally move an officer 14 fromone area to a different area.

Note that both of these types of information can be displayed on thesame map. Such a map would show both the location of underutilizedparking enforcement officers 14 and the location of higher valueassignments that currently have no officers 14 or too few officers 14 tosupport the assignments. By looking at such a map, a viewer can findavailable parking enforcement officers 14 to fill higher valueassignments who are already in a nearby area, so that travel time willbe minimized, and who are also currently in relatively low valueassignments, so that expected revenue is increased. If desired, thedispatcher 13 can also have the response plan generator 77 component ofthe planner and recommender layer 62 (shown in FIG. 4) run a planningalgorithm to suggest a reassignment plan that takes into account bothrevenue increase and travel time.

Scenario 4—Supporting Dispatchers

This scenario focuses on the situation assessment and responseactivities of a dispatcher 13 during part of a shift and describes thereal time assessment of unplanned events and how the human-plus-computerteam gathers the relevant information and makes decisions. In the samemanner as each parking enforcement officer 14, each dispatcher 13 has ac-partner. The scenario illustrates how all of these computers andpeople are engaged to make optimal decisions quickly while minimizinginterruptions to people.

Throughout the day, dispatchers 13 watch over their assigned regions ofOcean City. By the time that the parking enforcement officers 14 hit thestreets, the overall staffing and assignments have already beendeveloped; however, unplanned events often arise over the course of aday. Unplanned events include traffic accidents, roadway hazards,dangerous conditions that affect citizens and property, unscheduledcivic demonstrations, fires, and so on. When an unplanned event, as wellas planned events, occur, parking enforcement officers 14 may be calledupon to stop whatever they are doing at that moment and be asked tohandle the event by directing traffic or assisting in some other way. Toavoid lapses in parking enforcement coverage and help optimizeorganizational performance, dispatchers 13 need to adjust officerassignments in real time.

In this scenario, Dispatcher Dance monitors the activities of two squadsthat each have nine officers assigned. Both squads are organized inteams ranging from one to three officers apiece. Scenario 3 modifies andextends Scenario 2. The scenario begins with a shift handoff fromDispatcher Donavan to Dispatcher Dance, whilst a fire event from theprevious night is still being wrapped up. Early in Dance's shift, afalling tree hits a power line and a parking enforcement officer 14 mustbe assigned to respond to that event, assess the situation, call for anyadditional help if needed, and direct traffic. Scenario 3 also describesa response to a traffic accident that occurs later on in the day.

Here, Dispatcher Dance uses a situation assessment and planninginterface of the system. Phone support for 9-1-1 and 3-1-1 calls andother communications are integrated into the interface.

Conflicting Goals and Better Coordination Between Supervisors andDispatchers

There is overlap between the activities of dispatchers 13 andsupervisors 12. For the most part, dispatchers 13 are concerned withhandling unplanned events. Since dispatchers 13 are traditionally not asconcerned about the revenue performance of a parking enforcementorganization, their decisions tend to be blind to revenueperformance-related issues and, in some situations, their handlingunplanned events can significantly interfere with the revenue objectivesthat most concern supervisors 12. Consequently, decisions by dispatchers13 can disrupt the other goals for the organization. Here, this scenariofocuses on augmentations to the interfaces used by dispatchers 13 toprovide more visibility and accountability to a more complete range ofperformance goals of an enforcement organization than conventionallymade available to dispatchers 13.

Similarly, although supervisors 12 often have place a greater emphasison compliance and revenue concerns, supervisors 12 are also sometimesinvolved in responses to unplanned events. Here, the features of theinterfaces for dispatchers 13 can be included in the interfaces forsupervisors 12, and vice versa, as appropriate to the implementation.

Shift Handoff and Activity Monitoring

Date/Time Activity 8:01 a.m. Dispatcher Dance takes over the shift fromDispatcher Donavan. They use the situation assessment interface todiscuss the current status of events in Ocean City and view the ongoingsituation before Donavan goes home. Among other things, they review thestatus of a downtown fire event #975 at a Woolworth's store that startedon the previous day. The fire is now extinguished, but there is stillclean up and ongoing traffic control at the scene. Officer Fontaine fromthe previous shift plans to cover the scene until 9:00 a.m.

FIG. 47 is a diagram showing, by way of example, a situation assessmentinterface display 600 of the real time monitoring application of FIG.46. Episodic analytics interfaces can be included as part of thesituation assessment interface 600. They are used to review the previoustime course of an event. The episodic analytics include a record ofcommunications for responding to an event and a time-based animated map,which can show the positions and activities of various responders at anyprevious time during the course of the event.

Activity Recommendations for an Unplanned Event

Supervisors 12 and dispatchers 13 have oversight over the same teams ofofficers 14. Supervisors 12 are responsible for planning and achievingoverall performance, including planned revenue-bearing services andplanned service activities, but not the unplanned events and emergenciesthat are the purview of dispatchers 13. As well, due to the nature oftheir jobs, dispatchers 13 tend to ignore everything outside ofemergencies and lack the bandwidth to deeply understand the immediatesituations facing officers 14 on patrol on the streets.

Rather, dispatchers 13 are properly focused on handling emergencies, butif their decisions are completely uninformed, two types of concernsarise. First, the wrong officers 14 could be pulled off their currentactivities in circumstances in which pulling other available officers 14off their beats could meet the needs of the emergency equally well,which can adversely affect team performance. Second, the need toreassign officers 14 to cover the activities of those officers 14assigned to respond to an unplanned event remains unaddressed, whichfurther exacerbates team performance.

Here, simple evaluation criteria, such as time to respond or skills, isprovided to help dispatchers 13 make good assignments for handlingemergencies. Also, to provide additional criteria that help sensitizedispatchers 13 to avoiding decisions that unnecessarily impact overallperformance, sensor information is fused with current and historicalcitation data from the time-based active representational model 83(shown in FIG. 4) to illustrate the potential impact on overall teamperformance that would occur when officers 14 are pulled off theirbeats. The potential impact is quantified based upon the anticipatednumber of parking violations that would potentially be lost for each ofthe candidate officers 14, although other evaluation criteria arepossible.

The simple and additional evaluation criteria enable dispatchers 13 tomake better choices. Additionally, these evaluation criteria allow theweighing in of other metrics, such as public safety needs that includeresponse time, officer qualifications and number of officers needed,citation efficiency that includes projected number of citations lost asto each officer 14 assigned to respond to the unplanned event versusavailability of other officers to handle the citations otherwise lost,and balancing the load of each officer 14 assigned to respond to theunplanned event, travel time, and other policy considerations.

Date/Time Activity 8:15 a.m. A tree falls at 18^(th) Avenue and GrantStreet in Beat 3. The tree hits some power lines and live power linesare dangerously sparking in the street. A citizen calls 9-1-1. The 9-1-1call is routed to Dance, who logs the event as #989 and type #33, whichis a code referring to a potentially life-threatening obstacle on theroadway. This code gives the event Priority 1, which is a code Red toppriority. 8:15 a.m. Dance notices that the location of the event is inBeat 3. The closest officers are Adams (badge #79) and Baker (badge#99). Both officers are currently working on Priority 3 (low) servicecalls to monitor and direct traffic at school drop off. Cooper (badge#123) is the next closest officer. Cooper's predicted travel time to theevent is four minutes, which is within policy guidelines. Cooper iscurrently working with another team in Squad 1. The response plangenerator 77 suggests assigning Cooper to direct traffic for event #989.The system has already communicated with Cooper's c- partner to verifythat he is potentially available and not occupied on a higher priorityactivity. Dance clicks on “initiate plan,” which starts a series ofcommunications asking Cooper to accept the assignment. If Cooperaccepts, the response plan generator 77 will also prepare a plan tobalance the load of the other officers in Squad 1, Adams and Baker.

FIGS. 48-49 are diagrams showing, by way of examples, the situationassessment interface 610, 620 of the real time monitoring application ofFIG. 46. The situation assessment interfaces 610, 620 as implementedincludes the following components:

-   -   A sorted list of ongoing events at the top left. The most urgent        events for dispatcher attention appear at the top. The list        shows information about each event, such as the start time,        priority level, status, assigned officers, resources, and notes.    -   A set of recommended options that can be selected or modified.        The recommended options can include those officers 14 who are        presently on duty with a simple evaluation criteria that        includes time to respond, plus additional criteria that the        dispatcher 13 can consider when evaluating candidate officer        assignments, such as the impact on revenue or other costs that        assigning each of the officers would have on the overall        performance of the team.    -   A zoomable situation map showing the locations of active events,        plus the locations and duty status of each officer. When the        dispatcher 13 selects a particular event, the map scrolls and        zooms to the location of the event, showing a close-up of the        event.    -   A log and real time feed of communication events for each event.        Selecting a particular event brings up the log for the event.    -   A communications window for quickly contacting people.        Communications can be to multiple people at once and can be or        voice or text. Voice may be dynamically converted to text and        added into the same visual log.        Other screen components are possible.

The situation assessment interface shows recommended options forresponding to the fallen tree event. Here, the response plan generator77 of the planner and recommender layer 62 has proposed three candidateplans for responding to the event. The map shows the positions ofparking enforcement officers 14 relative to the location of the event.The costs column reflects an estimate of revenue that would potentiallybe lost from citations what would otherwise have been issued, ifapplicable, or other costs associated with each option. A real time feedshows the communications stream and summarizes the course of the event.Optionally, other elements of episodic analytics may be included.

Date/Time Activity 8:45 a.m. Dance is monitoring the fire event #975. Analert appears on the situation assessment dashboard saying that Fontaine(badge #33) will soon go off shift. Dance uses the communicationsability of the situation assessment interface 70 of the user interactionlayer 62 (shown in FIG. 4) to call Fontaine, who has finished directingtraffic and is presently collecting the traffic cones at the fire scene.There is no need to assign a replacement officer to the event. Dancechanges the status of event #975 to “Cleared.” 9:45 a.m. The powercompany and tree cutting crews have arrived for event #989 and haverespectively started work securing the power lines and removing the treefrom the roadway. They brought their own staff for directing traffic atthe scene. Cooper signs out from the event. Cooper gets a preliminaryview of his work on his shift using the “Plan My Shift” interface on hismobile computing device. The resource allocator 76 of the planner andrecommender layer 62 recommends that he resume the earlier plan from hissupervisor 12 and join Adams and Baker on Squad 3. The resourceallocator 76 of the planner and recommender layer 62 the assignment,which Cooper accepts. 10:00 a.m. Dance focuses on other squads and areassince not much is happening for Squads 1 and 2.

Balancing Work Load and Handling an Unplanned Event

External events, such as emergencies, can lead to dynamic reassignmentsof officers 14, usually by way of a dispatcher 13. If a supervisor 12 isnot available, the response plan generator 77 can recommend adjustmentsto the operational plans throughout the day as the situations change andthe coverage planner 78 can rebalance the remaining activities among theofficers 14 remaining on a team after one or more of the officers 14 areassigned to handle an unplanned event. The new optimal assignments arethen presented to officers 14 through the officer interface 47. To helpa dispatcher 13, super-beat team, or other manager reassign officersappropriately, the system 20 provides both information about theidentity and location of parking enforcement officers 14 on low valueassignments and the location and potential value of alternativeassignments within the city.

When officers 14 are assigned to pick up tasks that were started byother officers 14 who have been assigned to respond to an unplannedevent or emergency, the remaining unassigned officers 14 need to haveenough information displayed to enable them to gracefully continue thework of the team. For example, the officers 14 may need informationabout what areas were adequately covered recently, what vehicles havebeen marked by other officers 14 and may be ready to be checked forover-time violations, what service tasks remain unfinished, and soforth.

Policy can also have an influence on team performance and additionalcriteria can also be provided to assist dispatchers 13 with makingappropriate policy-adherent choices. For instance, officers 14 may beexpected to stagger their major breaks when operating as a team, so thatat least one officer is on duty on a super-beat to respond to anyaccident or urgent unplanned event. A dispatcher 13, however, may notknow which of the officers 14 on a team are on break at any given time.

Here, conditional autonomous messaging, as provided through theconditional autonomous messaging 73 component of the computer partnerand communications layer 63 (shown in FIG. 4), can effectively mediatebetween a dispatcher 13, via the parking enforcement support servicesserver 21, and team members, over the network through their respectivemobile devices, when the dispatcher 13 is actively vetting candidateofficer assignments. Typically, the officers 14 are equipped with bothconventional forms of communications, including radio- and cellularphone-types of devices and mobile devices that includewirelessly-connectable digital computing devices, such as personal,notebook and tablet computers, and so-called “smart” mobile computingdevices 26, such as smartphones and the like. Each officer's c-partner,as implemented through his mobile device, is able to autonomouslydetermine the officer's on-going status and his availability toundertake a new assignment based upon the officer's tracked activitiesand other information, such as messages sent or received by the officer,notes that the officer has taken, data which he has collected, and soforth. Conditional autonomous messaging is performed without theofficer's actual involvement. In one embodiment, a “push” communicationsmodel is used, where each officer's c-partner, that is, mobile device,periodically updates the parking enforcement support services server 21,such as every few minutes, by sending an update on the officer 14. In afurther embodiment, a “pull” communications model is used, where theparking enforcement support services server 21 queries each officer'sc-partner and receives an answer containing the update on the officer14. The “pull” model has the advantage of presumably beingup-to-the-second, yet a disruption in communications, such as a spotwith bad cellular coverage, can cause the c-partner to miss receipt of apull request from the server. On the other hand, the “push” model hasthe advantage of overcoming communication coverage lapses, but requiresmore power consumption by each mobile device and more data than requiredby the server could be sent. Thus, in a still further embodiment, ahybrid model that uses a combination of both “push” and “pull”communications models is used.

Date/Time Activity 1:05 p.m. The 9-1-1 operator takes a call about anaccident. The call is routed to Dance, who collects and enters theaccident information into the situation assessment interface 70 of theuser interaction layer 62 (shown in FIG. 4) as event #992. In someorganizations, there is a similar process followed when logging 9-1-1calls, except that the event information is collected and entered by the9-1-1 operator. In area A1, an SUV has rear-ended a minivan. Theresource allocator 76 of the planner and recommender layer 62automatically requests information about officer availability. Thec-partners of the parking enforcement officers 14 use conditionalautonomous messaging and information facilities in the system 20 torespond with availability information about location, predicted responsetime, current activities, and estimates of revenue (cost). 1:06 p.m.Baker (badge #99) is seven minutes away from the location of theaccident and on break. Policy discourages interrupting parkingenforcement officers' breaks for responding to an event, unless theevent is a Priority 1. Baker's c-partner responds to the query andindicates non- availability because she is on break. Adams (badge #79)is also seven minutes away, but is collecting tickets in the businessdistrict. Interrupting his work would have a “medium” cost. Hisc-partner acknowledges the query and responds with the information thathe is available, but with a medium revenue impact. Cooper (badge #123)is two minutes away patrolling a residential district. The potentialrevenue impact is low. Cooper's c-partner acknowledges Cooper'savailability for the assignment. 1:07 p.m. The situation assessmentinterface 70 of the user interaction layer 62 (shown in FIG. 4) receivesthe responses from the officers' c-partners and displays the informationon Dance's dashboard. The “Alerts” window adds a message that arecommendation is ready, that the best option is to assign Cooper toassess the traffic accident event. Alternatively, the assignment ofCooper could be completely automatic. For example, the situationassessment interface 70 of the user interaction layer 62 (shown in FIG.4) could display a countdown timer for 30 seconds or other interval andindicate to Dance that Cooper will be automatically assigned if shetakes no action before the timer runs out. This increased proactiveversion is useful for circumstances where dominant options aredetermined with high confidence by the system and the dispatcher 13 isbusy with something else at the moment. In effect, an automatic responsecould be delegated under department policy to automatically assignCooper to cover the traffic accident.

Dance uses the situation monitoring and assessment interface to enterinformation about a traffic accident, get recommendations, and initiatea response.

Adjusting Plans to Re-Allocate Activities as Needed

The reassignment of officers 14 is not the responsibility of thedispatcher 13. Rather, reassignment is a complex process akin to thedecisions made by a supervisor 12 of which officers 14 to assign towhich tasks. Reassignment involves the same situation awareness (datafusion), planning, and estimating that goes into making recommendationsto officers 14 as to where to patrol and which activities to perform,albeit with fewer resources and a focus on prioritization andoptimization. Thus, reassignment must take into account the anticipatednumber of parking violations for all of the officers 14 on a team andthe system 20 needs to plan patrol routes within a beat for the team'sremaining officers 14 based upon the overall anticipated number ofparking violations. This approach focuses on best utilizing theresources available following the assignment of officers 14 away fromthe team to respond to unplanned events and thereby minimize the impacton overall performance. The reassignments can be made dynamically to theofficers 14 in the form of recommendations or can be provided tosupervisors 12 for their consideration and action.

Date/Time Activity 1:08 p.m. Dance initiates the plan. A message alertappears on Cooper's mobile computing device and provides him with theinformation about the traffic accident. He is asked whether he acceptsthe assignment. Cooper sees the alert and accepts the assignment. Hedrives to the accident. 1:09 p.m. Cooper's response is shown on thesituation monitoring and assessment interface. The event monitor 80 ofthe monitoring layer 65 gathers and records information for episodicanalysis of the traffic event. The system notices that Cooper is overdueat the accident scene. However, he is still within the policy-dictatedtime window and is predicted to be about a minute away. The system doesnot send him an alert. 1:09 p.m. Meanwhile, the resource allocator 76 ofthe planner and recommender layer 62 reexamines the plans for the restof the team. Before being assigned to the event, Cooper was assigned toperform parking enforcement in a business district with a relativelyhigh number of expected citations. The optimizer of the resourceallocator 76 of the planner and recommender layer 62 generates andevaluates a set of revised plans that re-assign the dropped areas amongthe available officers, Adams and Baker. Since a redistribution of theareas to cover will produce a more optimal plan, the coverage planner 78of the planner and recommender layer 62 recommends that Adams change theareas that he will cover. He will pick up the busy part of Cooper'sassignment and drop a less productive part of his previous assignment.

FIG. 50 is a pair of diagrams 630 showing, by way of example,re-assignments of duty shift tasks. Here, at 1 p.m., when Cooper isre-assigned to the accident, the optimizer of the resource allocator 76of the planner and recommender layer 62 reexamines the opportunities andrecommends a different plan for Baker and Adams, which picks up some ofCooper's assignment and drops some of Adams' previous assignment. Thesechanges provide a more optimal way of achieving compliance.

Monitoring Plans and Escalating a Response as Needed

Once resources have been assigned, dispatchers 13 still need to maintainawareness of whether the situation is evolving appropriately. The system20 can monitor a preset time to respond threshold and alert a dispatcher13 when assigned officers 14 are not arriving within the expected timeto respond. The system 20 can also follow the assignment of resourcesand the progression of the situation and alert the dispatcher 13 whetheradditional resources are needed.

Date/Time Activity 1:10 p.m. Cooper arrives on the scene of theaccident. A person is trapped in one of the vehicles and gasoline isleaking onto the road. Cooper recognizes the situation as potentiallylife- threatening. He calls the dispatcher 13 for immediate escalationfor fire department support, with a request for an ambulance and othersupport. 1:10 p.m. Dance contacts 9-1-1 for fire, police, and ambulancesupport for the accident. She escalates the priority of the accident toPriority 1 code red. The system recognizes the situation as requiringadditional support for traffic control. The closest officer is nowOfficer Manning, who is passing through the beat as he returns fromanother assignment, but is assigned to an adjacent beat. This planrequires approval of Dispatcher Stephan, who oversees Manning's beat.1:12 p.m. Stephan, handing the nearby beat, sees the request for Manningon a Priority 1 event. He approves the resource request to assignManning to Dance. The resource allocator 76 of the planner andrecommender layer 62 updates the plans with the additional assignment ofManning. The top-ranked plan shows a response time of 2 minutes sinceManning is close by. Dance approves and puts the plan into action.Conclusion

The foregoing system 20 provides several benefits, including:

-   -   Providing displays showing sensed and predicted nearby events in        real time to individual officers 14, so that they can adjust        their activities based upon the real time data.    -   Providing just-in-time contextual information about past        performance and service requests to officers 14.    -   Communicating officer observations and data about situations        back to the system 20.    -   Monitoring the activities of officers 14 and others and        determining how busy they are, whether they are multitasking,        the priority of what they are doing, and when interrupt them or        presenting information to them is reasonable.    -   Adjusting and prioritizing the presentations of information to        officers 14 and dispatchers 13 to prevent information overload.    -   Providing long-term memory of situations and trends about city        events to make predictions of enforcement opportunities, even        when some data about actual events is not directly visible.    -   Checking in with officers 14 about availability to commit to        service request commitments.    -   Enabling c-partners to answer service requests and provide other        information dynamically when possible without interrupting the        officers 14.    -   Computing and representing availability and workload        (“busyness”) information as part of the feasibility and cost        evaluation for competing plans.    -   Predicting the outcomes of possible activities in the        environment, including officer activities, citation lifecycles,        emerging planned and unplanned events, and traffic flow.    -   Using information from such predictions to help evaluate        competing recommendations and activity assignments.    -   Using predictions to compare the outcome of what people are        doing to what has been recommended, which can then be used in        review, to improve the model, or to focus alerts and attention.    -   Providing intelligent interfaces for overseeing activities of        teams and directing management attention to places where        effective advice or communications can most improve performance.    -   Providing recommendations to dispatchers 13 and supervisors 12        about team activities that optimize organization performance,        rather than individual performances.    -   Giving focus recommendations to officers 14, monitoring        activities to see whether they take the advice generally, and        comparing performance of officers 14 that follow recommendations        to those that do not.    -   Providing visualizations for combining time and location        dimensions to visualize routes or paths along with the ability        to view progress and event markers.    -   Running simulations of city situations to predict the costs and        benefits of various enforcement policies.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A system for monitoring parking enforcementofficer performance in real time with the aid of a digital computer,comprising: one or more sensors placed in different locations within acity and configured to obtain information; at least one automaticlicense plate reader (ALPR) to implement a secure communication schemefor transmitting recorded license plates; a storage device, comprising:a definition of a beat within the city for a parking enforcement officerwithin which enforcement activities are to be performed by the officer;and a time-based active representational model of the city thatcomprises estimates of parking violations expected to occur within thebeat and which fuses parking citation data with the information receivedfrom the sensors in the city and data transmitted directly from therecorded license plates via the ALPR, wherein the time-based activerepresentational model is updated in real time; a processor and memorywithin which code for execution by the processor is stored, furthercomprising: an activity planner adapted to build a plurality activityplans for patrol by the officer in the city based upon the fusedinformation from the active representational model, and to select thoseactivity plans that optimize performance by the officer from theplurality of activity plans; an observer adapted to regularly trackactivities of the officer while on the beat; and an officer monitoradapted to create analytics based upon differences between the officer'sexpected performance for patrol in the city according to the optimalactivity plans, which are selected from a plurality of activity plans,versus the officer's actual performance of patrol in the city accordingto the officer's tracked activities determined in real time; a personalcomputer that executes a situation assessment interface and ismaintained by a supervisor or other individual to receive the analyticsfrom the processor, to determine low performance by the officer inreal-time, and to transmit instructions for patrol by the officer fromthe supervisor or other individual based on the analytics, wherein thelow performance is determined based on a low level of activity detectedby a lapse in activities performed by the officer; and the personalcomputer that generates a display with a location of the officer via anindicator on a map to indicate the low performance of the officer.
 2. Asystem according to claim 1, further comprising at least one of:historical enforcement data collected from past parking violationswithin the beat as part of the parking citation data; and currentenforcement data derived from the officer's tracked activities while onthe beat as part of the parking citation data.
 3. A system according toclaim 1, further comprising at least one of: characteristics pertainingto the beat included when building the activity plans from the groupcomprising one or more of nature of the beat, time of day, and day ofweek, season, number of officers on the beat, traffic conditions, andservice requirements; and factors specific to the officer included whenbuilding the activity plans from the group comprising the officer'slevel of experience and familiarity with the beat.
 4. A system accordingto claim 1, further comprising: the storage further comprisingexpectations for the officer based upon the optimal activity plans; andthe processor and memory further comprising: a motivation recommenderadapted to provide the expectations to the officer, and to includewhether the officer met the expectations as part of the analytics.
 5. Amethod for monitoring parking enforcement officer performance in realtime with the aid of a digital computer, comprising the steps of:defining a beat within a city for a parking enforcement officer withinwhich enforcement activities are to be performed by the officer;monitoring sensors in the city and automatic license plate readers(ALPRs), which each implement secure communication schemes; fusingparking citation data with information received from the sensors in thecity and data transmitted directly from at least one ALPR into atime-based active representational model of the city that comprisesestimates of parking violations expected to occur within the beat,wherein the time-based active representational model is generated andupdated in real time; building a plurality activity plans for patrol inthe city for the officer based upon the fused information from theactive representational model; selecting those activity plans thatoptimize performance for patrol in the city by the officer from theplurality of activity plans; regularly tracking activities of theofficer while on the beat; creating analytics based upon differencesbetween the officer's expected performance for patrol in the cityaccording to the optimal activity plans, which are selected from aplurality of activity plans, versus the officer's actual performance ofpatrol in the city according to the officer's tracked activitiesdetermined in real time; transmitting the analytics to a situationassessment interface executed by a personal computer available to asupervisor or other individual; determining low performance by theofficer in real-time, wherein the low performance is determined based ona low level of activity detected by a lapse in activities performed bythe officer; displaying a location of the officer via an indicator on amap to indicate the low performance of the officer; and receivinginstructions for patrol by the officer from the supervisor or otherindividual based on the analytics.
 6. A method according to claim 5,further comprising at least one of the steps of: collecting historicalenforcement data from past parking violations within the beat as part ofthe parking citation data; and deriving current enforcement data fromthe officer's tracked activities while on the beat as part of theparking citation data.
 7. A method according to claim 5, furthercomprising at least one of the steps of: including characteristicspertaining to the beat when building the activity plans from the groupcomprising one or more of nature of the beat, time of day, and day ofweek, season, number of officers on the beat, traffic conditions, andservice requirements; and including factors specific to the officer whenbuilding the activity plans from the group comprising the officer'slevel of experience and familiarity with the beat.
 8. A method accordingto claim 5, further comprising the steps of: setting expectations forthe officer based upon the optimal activity plans; providing theexpectations to the officer; and including whether the officer met theexpectations as part of the analytics.
 9. A method according to claim 8,further comprising the step of: quantifying the expectations based uponat least one of productivity, adherence to following recommendations,activities within beat by other officers within the officer's beat,comparison to performance of other officers, metrics for working hard,and completion of service assignments.
 10. A method according to claim5, further comprising the steps of: planning a performance-optimizedpatrol route for the officer comprising estimated parking violations perblock within the beat; providing the performance-optimized patrol routeto the officer as a recommendation; and including whether the officerundertook the recommendation as part of the analytics.
 11. A methodaccording to claim 5, further comprising the steps of: followingassignment of the officer to either perform a planned event or respondto an unplanned event, building one or more updated activity plans foreach of the officers remaining on a team in which the assigned officeris comprised based upon the fused information from the activerepresentational model; and identifying the updated activity plans thatoptimize performance by the remaining officers.
 12. A method accordingto claim 5, wherein the activities of the officer comprise one or moreof issuing citations, directing traffic, working at a planned event, andresponding to an unplanned event.
 13. A method according to claim 5,wherein the analytics comprise one or more of lack of progress, lowproduction rate of citations, not moving, not covering areas within thebeat according to policy, unaccounted gaps during which no activitieshave been performed, not taking a break as required, and neglectingtimed parking zones ready for coverage.
 14. A method according to claim5, further comprising the step of: determining the activities of theofficer with the aid of one or more of a mobile computing device,parking meter sensor, log of tagged communications, voice activatedsystem, and centralized citation database.
 15. A method according toclaim 5, further comprising at least one of the steps of: tracking thelocations of the officer at least one of on demand, periodically, or astriggered by performance of the activities; monitoring progress onperformance of the activities by the officer; and identifying activitiescomprising one or more of the activities nearing completion and theactivities taking longer than expected.
 16. A method according to claim5, further comprising at least one of the steps of: displaying theanalytics with a map comprising a representation of the beat, theofficer's tracked activities, and the enforcement activities performedby the officer; and presenting the analytics in a dashboard comprisingthe estimated parking violations in the optimal activity plans, theofficer's tracked activities, and the enforcement activities performedby the officer.
 17. A method according to claim 5, further comprising atleast one of the steps of: regularly tracking activities of the officerdirectly based upon locational data provided through GPS, Wi-Fi addresstables, and location sensing devices; and regularly tracking activitiesof the officer indirectly through traffic loop sensors, car markinginformation from ALPR-equipped vehicles, traffic flow sensor, and camerasensors.
 18. A method according to claim 5, wherein the sensors aroundthe city comprise one or more of location-sensing devices comprised inthe officer's vehicle or mobile computing device, traffic loop sensors,parking meter payment collectors, traffic flow sensors, ALPR sensingsystems on vehicles, and vehicle occupancy sensors associated withparking spaces.
 19. A method according to claim 5, wherein the beatconstitutes one of a zone, traditional beat, and super-beat.
 20. Anon-transitory computer readable storage medium storing code forexecuting on a computer system to perform a method, comprising: defininga beat within a city for a parking enforcement officer within whichenforcement activities are to be performed by the officer; monitoringsensors in the city and automatic license plate readers (ALPRs), whicheach implement secure communication schemes; fusing parking citationdata with information received from the sensors in the city and datatransmitted directly from at least one ALPR into a time-based activerepresentational model of the city that comprises estimates of parkingviolations expected to occur within the beat, wherein the time-basedactive representational model is generated and updated in real time;building a plurality of activity plans for patrol in the city by theofficer based upon the fused information from the activerepresentational model; selecting those activity plans that optimizeperformance for patrol in the city by the officer from the plurality ofactivity plans; regularly tracking activities of the officer while onthe beat; creating analytics based upon differences between theofficer's expected performance for patrol in the city according to theoptimal activity plans, which are selected from a plurality of activityplans, versus the officer's actual performance of patrol in the cityaccording to the officer's tracked activities in real time; transmittingthe analytics to a situation assessment interface executed by a personalcomputer available to a supervisor or other individual; determining lowperformance by the officer in real-time, wherein the low performance isdetermined based on a low level of activity detected by a lapse inactivities performed by the officer; displaying a location of theofficer via an indicator on a map to indicate the low performance of theofficer; and receiving instructions for patrol by the officer from thesupervisor or other individual based on the analytics.